A dynamic prompt word adaptation method based on target AI platform portrait

Abstract

This invention provides a dynamic prompt word adaptation method based on a target AI platform profile, comprising the following steps: receiving a generation request and parsing to obtain source content, a target AI platform identifier, and at least one additional generation constraint; extracting a corresponding platform preference object from a preset platform preference fingerprint database based on the target AI platform identifier; the platform preference object being a structured data set used to parameterize the target AI platform's multi-dimensional output preferences for the generated content; performing conflict detection between the platform preference object and the additional generation constraints and preset general generation rules, and performing arbitration according to preset priority rules to generate a unified constraint set; dynamically generating system-level instructions adapted to the target AI platform based on the unified constraint set; and inputting the system-level instructions and the source content into a generation model to obtain a text generation result adapted to the target AI platform.

Description

Technical Field

[0001] This invention belongs to the field of generative artificial intelligence content processing and intelligent distribution control technology, specifically involving a dynamic prompt word adaptation method based on the target AI platform profile. Background Technology

[0002] With the practical application of generative artificial intelligence (AI) technology, various AI content distribution channels, such as general-purpose dialogue models, generative search engines, conversational agent platforms, and AI content aggregation platforms, have become core entry points for enterprises and content producers to reach end users. Unlike traditional content distribution scenarios targeting human users, AI platforms exhibit significantly different processing logics for received content. Based on their product positioning, algorithm rules, and user group characteristics, different platforms impose differentiated requirements on content style, structure, citation methods, and information density. The compatibility between content and platform rules directly determines the content's crawling priority, ranking weight, and display effect on the corresponding platform. Therefore, adapting and optimizing content from the same source for multiple AI platforms has become one of the core requirements of the content production process. To address this need, the industry has begun exploring differentiated content generation technologies for different generative search engines, conversational agent platforms, or AI content aggregation platforms. The main idea is to establish a preference profile for the target platform and inject the relevant requirements of the preference profile into the prompt word construction process to achieve targeted adaptation and output of content from the same source on different target AI platforms.

[0003] However, such methods have obvious limitations in practical applications: First, platform preferences cannot be modeled in a structured way. Existing systems usually adjust the output style by hard-coding Prompt strings or a few fixed templates, lacking the ability to abstract the preferences of different AI platforms into parameterizable, callable, and extensible feature objects. In practical applications, as the number of adapted platforms increases, the template maintenance cost will rise linearly. Once the rules of a certain platform are adjusted, all related prompt word templates need to be manually modified one by one, resulting in extremely low reusability and high migration costs. Moreover, the quality of the templates is highly dependent on the writer's understanding of the platform rules. The output effect of templates written by different people varies greatly, and the stability of content adaptation is difficult to guarantee. Secondly, multiple constraints and conflicts cannot be precisely arbitrated. When there are overlaps or conflicts between target platform preferences, corporate brand tone, output task objectives, and structural strategies, existing solutions usually adopt simple overlay or manual splicing methods, which are difficult to complete priority judgment, conflict resolution, and stable output. Such conflicts are very common in real-world scenarios. For example, some platforms require content to be concise and objective, while brands require content to be lively and engaging. Existing methods require manual weighing and adjustment of conflicting requirements one by one, which is not only time-consuming and labor-intensive, but also prone to rule omissions. In particular, omissions of compliance constraints may lead to content being blocked by the platform, bringing unnecessary operational risks. Finally, the efficiency of adapting a single draft to multiple platforms is low. When facing multiple target AI platforms, existing technologies usually require manual writing of multiple versions of prompts or copy, and cannot automatically branch a single request into multiple platform-specific generation tasks and bind different output results, thereby increasing the cost of content production and distribution. Moreover, the requirements of current AI platforms for content have expanded from single text to multimodal formats. In addition to the main text, they usually require supplementary content such as titles, summaries, illustrations, and Q&A blocks. Existing adaptation methods usually generate content for each modality on each platform separately, which not only further reduces production efficiency, but also easily leads to inconsistencies in information across different modalities, affecting the consistent expression of brand content.

[0004] Overall, existing multi-AI platform content adaptation technologies are still in the stage of manual-led and fragmented processing, lacking a systematic adaptation framework and failing to meet the needs of large-scale, high-quality multi-platform content distribution. Therefore, there is an urgent need for a technical solution that can parameterize the preferences of target AI platforms, automatically construct dynamic prompts, perform multi-dimensional constraint arbitration, and generate multiple sets of differentiated content results to improve the adaptability and distribution efficiency of content on different AI platforms. Summary of the Invention

[0005] To address the shortcomings and deficiencies of existing technologies, this invention provides a method and system for dynamic prompt word adaptation and multimodal generation based on target AI platform profiles. This method constructs a platform preference fingerprint database, abstracting the output preferences of different AI platforms for generated content into structured platform preference objects that include content style features, citation style features, keyword density thresholds, evidence strength features, structural preference features, and length control features. Upon receiving a generation request containing source content, target AI platform identifiers, and additional generation constraints, the corresponding platform preference object is extracted and subjected to conflict detection with the additional generation constraints and general generation rules. Arbitration is performed according to preset priority rules to generate a unified constraint set. Based on this unified constraint set, system-level instructions adapted to the target platform are dynamically generated and input into the generation model to obtain the text generation result. This invention further provides a result verification mechanism, performing keyword density, citation integrity, and structural consistency checks on the generated results, and performing rewriting or regeneration when the checks fail. For multi-platform scenarios, the generation request can be copied into multiple branch requests, generating differentiated results corresponding to each platform and binding them to platform identifiers. Furthermore, multimodal results sharing the same constraint set can be generated based on text or differentiated results, ensuring the consistency of multimodal content. The corresponding system includes a request receiving and parsing module, a platform preference fingerprint database module, a constraint fusion and arbitration module, a dynamic prompt word construction module, and a generation and execution module, and can be expanded to include a result verification module, a concurrent distribution module, and a multimodal output module. This invention achieves parametric modeling of platform preferences, dynamic arbitration of multiple constraints, and automated adaptation, significantly improving the efficiency of content adaptation and distribution quality across different AI platforms.

[0006] The specific technical solution adopted by this invention to solve its technical problem is as follows:

[0007] A dynamic prompt word adaptation method based on the target AI platform profile includes the following steps:

[0008] Receive a generation request and parse it to obtain the source content, the target AI platform identifier, and at least one additional generation constraint;

[0009] Based on the target AI platform identifier, the corresponding platform preference object is extracted from the preset platform preference fingerprint database; the platform preference object is a structured data set used to parameterize the multi-dimensional output preferences of the target AI platform for the generated content.

[0010] The platform preference object is subjected to conflict detection with the additional generation constraints and the preset general generation rules, and arbitration is performed according to the preset priority rules to generate a unified constraint set.

[0011] Based on the unified constraint set, system-level instructions adapted to the target AI platform are dynamically generated.

[0012] The system-level instructions and the source content are input into the generation model to obtain text generation results adapted to the target AI platform.

[0013] Furthermore, the platform preference objects include at least content style features, citation style features, keyword density threshold, evidence strength features, structural preference features, and length control features.

[0014] Furthermore, the preset priority rules are sorted from high to low according to constraint priority as follows: citation and evidence constraints, structural constraints, task type constraints, brand tone constraints, and style fine-tuning constraints.

[0015] Furthermore, the system-level instructions are generated through a dynamic prompt word builder, and the system-level instructions include at least constraints on expression style, citation method, keyword density, structural format, evidence integrity, and output type.

[0016] Furthermore, after obtaining the text generation result adapted to the target AI platform, an adaptation check is performed on the text generation result, and if the check fails, rewriting, partial regeneration, or full regeneration is performed; the adaptation check includes keyword density check, citation integrity check, and structural consistency check.

[0017] Furthermore, when the target AI platform identifier in the generation request includes multiple target AI platforms, the generation request is copied into multiple branch requests corresponding to the number of target AI platforms. For each branch request, all steps from extracting platform preference objects to generating text generation results are executed to generate differentiated results that correspond one-to-one with each target AI platform, and each differentiated result is bound to the corresponding target AI platform identifier.

[0018] Furthermore, based on the text generation result or the differentiation result, a multimodal result adapted to the corresponding target AI platform is generated; the multimodal result and the corresponding text generation result or differentiation result share the same platform preference object and unified constraint set.

[0019] And, a dynamic prompt word adaptation system based on the target AI platform profile, including:

[0020] The request receiving and parsing module is used to receive generation requests and parse them to obtain the source content, the target AI platform identifier, and at least one additional generation constraint.

[0021] The platform preference fingerprint module is used to pre-store structured platform preference objects corresponding to multiple target AI platforms, and output the corresponding platform preference objects according to the target AI platform identifier; the platform preference objects are structured data sets used to parameterize the multi-dimensional output preferences of the target AI platform for the generated content;

[0022] The constraint fusion and arbitration module is used to perform conflict detection between the platform preference object and the additional generation constraints and the preset general generation rules, and to perform arbitration according to the preset priority rules to generate a unified constraint set.

[0023] The dynamic prompt word construction module is used to dynamically generate system-level instructions adapted to the target AI platform based on the unified constraint set;

[0024] The generation execution module is used to input the system-level instructions and the source content into the generation model to obtain text generation results adapted to the target AI platform.

[0025] Furthermore, it also includes a result verification module, which is used to perform adaptability verification on the text generation result, and to perform rewriting, partial regeneration or full regeneration when the verification fails; the adaptability verification includes keyword density verification, citation integrity verification and structural consistency verification.

[0026] Furthermore, it also includes a concurrent distribution module and a multimodal output module; the concurrent distribution module is used to copy the generation request into multiple branch requests corresponding to the number of target AI platforms when the target AI platform identifier in the generation request includes multiple target AI platforms, and schedule each module to generate differentiated results corresponding to each target AI platform; the multimodal output module is used to generate multimodal results adapted to the corresponding target AI platform based on the text generation result or the differentiated result.

[0027] And a computer system including a processor and a memory, the memory storing a computer program, the processor executing the computer program to implement the method described above.

[0028] A non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the method described above.

[0029] Compared to existing technologies, this invention and its preferred solution achieve structured and parametric modeling of content preferences for target AI platforms. It transforms the previously manually hard-coded prompt word templates into reusable, callable, and scalable standardized feature objects, significantly reducing the rule maintenance costs and manual reliance for multi-platform adaptation. This effectively solves the problems of poor template reusability, high migration costs, and insufficient stability of adaptation effects. A conflict detection and priority arbitration mechanism for multi-source constraints is established, automatically resolving and integrating conflicts arising from platform preferences, brand tone, task objectives, and general rules, eliminating the need for manual adjustments. This ensures the adaptability of output content to target platforms while maintaining brand consistency, and reduces operational risks associated with omissions in compliance rules. Furthermore, it enables the self-adaptation of content from the same source to multiple target AI platforms. Automated and concurrent differentiated content generation can automatically branch out and complete customized content output for multiple platforms based on a single generation request, eliminating the need for manual writing of multiple versions of prompts and copy, significantly improving the overall efficiency of content production and distribution across multiple platforms. A closed-loop adaptation verification mechanism for the generated results is constructed, which verifies and corrects the platform compatibility of the output content, ensuring that the output content meets the core rule requirements of the target platform. It can also achieve consistent generation of multimodal content under the same constraint system, avoiding information discrepancies between different modalities and ensuring the standardization and uniformity of brand content output. Overall, a systematic multi-AI platform content adaptation framework is constructed, breaking through the limitations of existing technologies that are manually dominated and fragmented, meeting the needs of large-scale, high-quality multi-platform content distribution, and effectively improving the adaptation effect and distribution value of content on target AI platforms. Attached Figure Description

[0030] The present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments:

[0031] Figure 1 This is a schematic diagram of the overall process of the method according to an embodiment of the present invention;

[0032] Figure 2 This is a schematic diagram of the platform preference fingerprint database and profile extraction process according to an embodiment of the present invention;

[0033] Figure 3 This is a schematic diagram of the dynamic prompt word construction and constraint arbitration process according to an embodiment of the present invention;

[0034] Figure 4 This is a schematic diagram of the multi-platform concurrent branch generation and result binding process in an embodiment of the present invention. Detailed Implementation

[0035] To make the features and advantages of the present invention more apparent and understandable, specific embodiments are described below in detail:

[0036] It should be noted that the following detailed descriptions are exemplary and intended to provide further explanation of this application. Unless otherwise specified, all technical and scientific terms used in this specification have the same meaning as commonly understood by one of ordinary skill in the art to which this application pertains.

[0037] It should be noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit the exemplary embodiments according to this application. As used herein, the singular form is intended to include the plural form as well, unless the context clearly indicates otherwise. Furthermore, it should be understood that when the terms "comprising" and / or "including" are used in this specification, they indicate the presence of features, steps, operations, devices, components, and / or combinations thereof.

[0038] To address the shortcomings of existing technologies, such as the inability to parametrically model target AI platform preferences, the inability to dynamically arbitrate multiple constraints, and the inability to efficiently achieve differentiated output across multiple platforms, this invention provides a method and system for dynamic prompt word adaptation and multimodal generation based on target AI platform profiles. Specifically, it involves a content differentiation generation technology for different generative search engines, conversational agent platforms, or AI content aggregation platforms. By establishing a preference profile for the target platform and parameterizing this profile into the prompt word construction process, it achieves targeted adaptation and output of content from the same source across different target AI platforms.

[0039] This invention establishes independent platform preference profiles for different target AI platforms, representing these profiles as a set of structured features. Upon receiving source content and target platform identifiers submitted by the user, the corresponding platform preference features are retrieved. These platform preference features are then fused with pre-set task strategies, brand tone strategies, and structural strategies to construct system-level instructions with constraints. Subsequently, the system-level instructions and source content are input into a generation model to obtain information generation results adapted to the target AI platform. When multiple target platforms exist, multi-path branching and generation are automatically executed, outputting differentiated results corresponding to multiple platforms.

[0040] This invention discloses a method and system for dynamic prompt word adaptation and multimodal generation based on target AI platform profiles. The method includes: receiving a generation request and parsing the source content, target AI platform identifier, task type, brand tone, structural strategy, and keyword set; constructing a platform preference fingerprint database and extracting the corresponding platform preference object based on the target AI platform identifier, wherein the platform preference object includes at least content style features, citation style features, keyword density threshold, evidence strength features, structural preference features, and length control features; performing conflict detection and priority arbitration on the platform preference object with brand tone, task type, structural strategy, and general generation rules to generate a unified constraint set; constructing system-level instructions based on the unified constraint set and inputting the system-level instructions and source content into the generation model to obtain the main text result adapted to the target AI platform; performing keyword density verification, citation integrity verification, and structural consistency verification on the main text result; when the target AI platform identifier includes multiple target AI platforms, the generation request is copied into multiple branch requests to generate corresponding differentiated results; and outputting multimodal results such as title candidate set, summary card, FAQ block, citation evidence block, and image generation prompt based on each differentiated result. This invention enables dynamic prompt word adaptation and differentiated content generation to meet the preferences of different AI platforms, thereby improving the platform adaptability, structural consistency, and multi-platform distribution efficiency of the generated content.

[0041] This invention first presents a method for dynamic prompt word adaptation and multimodal generation based on target AI platform profiles, comprising the following steps:

[0042] S1. Receive the generation request and parse the source content, target AI platform identifier, task type, brand tone, structural strategy, keyword set and user supplementary requirements in the generation request;

[0043] S2. Based on the target AI platform identifier, extract the platform preference object corresponding to the target AI platform identifier from the preset platform preference fingerprint database. The platform preference object includes at least the content style feature, citation style feature, keyword density threshold, evidence strength feature, structural preference feature and length control feature.

[0044] S3. Perform conflict detection and priority arbitration between platform preference objects and brand tone, task type, structural strategy and general generation rules to generate a unified set of constraints.

[0045] S4. Based on a unified set of constraints, a set of keywords, and user-supplemented requirements, a dynamic prompt word builder is used to generate system-level instructions.

[0046] S5. Input the system-level instructions and source content into the generation model to obtain the main text result adapted to the target AI platform;

[0047] S6. Perform keyword density verification, citation integrity verification, and structural consistency verification on the main text result, and perform rewriting, partial regeneration, or full regeneration if the verification fails.

[0048] S7. When the target AI platform identifier includes multiple target AI platforms, the generation request will be copied into multiple branch requests, generating differentiated results corresponding to each target AI platform, and binding the platform identifier to each differentiated result.

[0049] S8. Generate multimodal results adapted to the corresponding target AI platform based on each differentiated result.

[0050] In step S1, the source content includes one or more of the following: article draft, product introduction, Q&A text, marketing copy, and summary text; the task type includes one or more of the following: long article generation, Q&A generation, summary generation, FAQ generation, and graphic description generation.

[0051] In step S2, the platform preference fingerprint database uses a key-value mapping structure to store platform preference objects corresponding to multiple target AI platforms. The target AI platform identifier serves as the key-value index, and the platform preference objects are extracted as the index results. Content style features within the platform preference objects are used to limit the expression of the output content; citation style features are used to limit the citation method of factual statements; keyword density thresholds are used to limit the maximum allowed density of keywords in the output text; evidence strength features are used to limit the source support strength of the output content; structural preference features are used to limit the organization of heading levels, bullet point formats, summary blocks, or FAQ blocks; and length control features are used to limit the length levels of the output text. For example, the length control features are divided into three levels based on the number of tokens in the output text: short text level (within 500 tokens), Chinese text level (500-2000 tokens), and long text level (above 2000 tokens); alternatively, length thresholds can be set according to the number of Chinese characters required by the target AI platform. The evidence strength features are divided into three levels from low to high: Level 1 requires factual statements to be marked with corresponding source identifiers; Level 2 requires factual statements to be accompanied by traceable source links; Level 3 requires factual statements to provide authoritative source endorsement and multi-source cross-verification evidence. The corresponding strength level can be set according to the requirements of the target AI platform. The preset value range for the keyword density threshold is 0.005-0.03, that is, the upper limit of the frequency of keyword occurrence in the output text is 0.5%-3%, which can be flexibly adjusted according to the inclusion rules of the target AI platform.

[0052] Content style features include one or more of the following: professional, concise, structured, and natural; citation style features include one or more of the following: inline citation, footnote citation, source link citation, and academic citation; keyword density threshold is a preset floating-point threshold.

[0053] In step S3, priority arbitration shall be performed in at least the following order: citation and evidence constraints take precedence over structural constraints, structural constraints take precedence over task type constraints, task type constraints take precedence over brand tone constraints, and brand tone constraints take precedence over style fine-tuning constraints. When there is a conflict between the platform preference object and the brand tone, the rigid constraints of the target AI platform shall be maintained first, and non-rigid style constraints shall be compromised and integrated.

[0054] As a further preferred implementation method, the compromise integration of non-rigid style constraints is carried out according to the following rules: when there is a conflict between the platform's preferred style and the brand's tone in the non-rigid style dimension, the style requirements of the platform's preference are used as the benchmark, and the style characteristics of the brand's tone are integrated without breaking the platform's rigid constraints; for example, when the platform requires a concise content style and the brand requires a professional content style, the final integration is: a professional and rigorous concise expression, retaining core professional information and removing redundant embellishments.

[0055] In step S4, the dynamic prompt word builder includes at least a task intent pool, a format constraint area, and a keyword requirement area. The dynamic prompt word builder first generates a task intent description based on the task type, then injects a unified constraint set into the format constraint area and a keyword set into the keyword requirement area, generating system-level instructions that include expression style constraints, citation method constraints, keyword density constraints, structural format constraints, evidence integrity constraints, and output type constraints.

[0056] In step S5, when the target AI platform's platform preference object contains concise content style characteristics and source link citation characteristics, the system-level guidance includes the constraints of "answering in a concise manner" and "attaching source links or footnotes to factual statements"; when the target AI platform's platform preference object contains structured content style characteristics and academic citation characteristics, the system-level guidance includes the constraints of "organizing content using structured information blocks" and "enhancing credible academic endorsement expressions".

[0057] In step S6, keyword density is obtained by the ratio of the number of times the target keyword appears in the main text result to the total number of words, total number of tokens, or total number of measurable characters in the main text result; when the keyword density is greater than the keyword density threshold, one or more of the following are performed: keyword replacement, keyword deletion, synonym replacement, or partial rewriting; citation integrity check is used to detect whether factual statements contain source identifiers corresponding to citation style features.

[0058] In step S7, after the generation request is copied into multiple branch requests, different platform preference objects are loaded for each branch request, different system-level instructions are constructed, and the same or different generation models are called to generate multiple differentiated results; the differentiated results are bound to their corresponding target AI platform identifier, generation timestamp, structure type and reference mode.

[0059] In step S8, the multimodal results include one or more of the following: title candidate set, summary card, FAQ block, cited evidence block, illustration caption text, and image generation prompt; each multimodal result shares the same platform preference object and unified constraint set with the corresponding main text result to maintain consistency in style, structure, and citation method.

[0060] As a further preferred implementation, when generating multimodal results, the platform preference object and unified constraint set that have been arbitrated in the corresponding main text result branch are directly reused, without having to re-execute the constraint detection and arbitration process; when generating multimodal content such as title candidate set, summary card, FAQ block, and image generation prompts, the style, structure, and citation rules in the unified constraint set are strictly followed to ensure that the expression specifications of all modal content are completely consistent.

[0061] Corresponding to the above methods, the present invention also provides a dynamic prompt word adaptation and multimodal generation system based on the target AI platform profile, comprising:

[0062] The request receiving and parsing module is used to receive generated requests and parse the source content, target AI platform identifier, task type, brand tone, structural strategy, keyword set and user supplementary requirements;

[0063] The platform preference fingerprint module is used to store platform preference objects corresponding to multiple target AI platforms, and output the corresponding platform preference objects according to the target AI platform identifier;

[0064] The constraint fusion and arbitration module is used to perform conflict detection and priority arbitration between platform preference objects and brand tone, task type, structural strategy and general generation rules, and generate a unified set of constraints.

[0065] The dynamic prompt word construction module is used to generate system-level prompts based on a unified set of constraints, a set of keywords, and user-supplemented requirements.

[0066] The generation execution module is used to input system-level instructions and source content into the generation model to generate the main text result adapted to the target AI platform;

[0067] The result verification module is used to perform keyword density verification, citation integrity verification, and structural consistency verification on the main text result, and trigger rewriting, partial regeneration, or full regeneration when the verification fails.

[0068] The concurrent distribution module is used to copy the generation request into multiple branch requests when the target AI platform identifier includes multiple target AI platforms, generate corresponding differentiated results and bind them to the platform identifiers respectively;

[0069] The multimodal output module is used to generate multimodal results based on the differentiated results.

[0070] As a more specific implementation method:

[0071] Firstly, such as Figure 1 As shown, this embodiment of the invention provides a method for dynamic prompt word adaptation and multimodal generation based on a target AI platform profile, including the following steps:

[0072] S1. Receive the generation request and parse the input parameters;

[0073] Obtain the source content to be optimized, the target AI platform identifier, task type, brand tone, structural strategy, and keyword set.

[0074] The source content can be an article draft, product introduction, Q&A text, marketing copy, summary text, or a combination thereof; the target AI platform identifier is used to represent the platform to be adapted; the task type is used to represent whether the task generated this time is long article generation, Q&A generation, summary generation, FAQ generation, graphic description generation, or a combination thereof; and the brand tone is used to represent the company's preset expression style.

[0075] The step of "receiving user requests and calling the basic generation model" is a routine input processing flow in existing content generation systems; while the structured analysis of the target AI platform identifier, brand tone and structural strategy in the input, as a unified entry point for subsequent profile matching and constraint arbitration, is a preliminary organizational step in the overall solution of this invention.

[0076] As a further implementation method, upon receiving a generation request, the request content is parsed in a structured manner to obtain standardized input parameters, including source content, target AI platform identifier, task type, brand tone, structural strategy, keyword set, and user supplementary requirements. The source content may include article drafts, product introductions, Q&A text, marketing copy, and summary text; the task type may include long-text generation, Q&A generation, summary generation, FAQ generation, and graphic description generation; the brand tone may include enumerated values ​​such as technological, approachable, professional and rigorous, and lively.

[0077] This step provides a unified, standardized input entry point for the entire process, avoiding subsequent processing deviations caused by unstructured requests.

[0078] As a further preferred implementation, the parameters after structured parsing are stored in JSON format with fixed fields for easy retrieval in subsequent steps, as shown in the example below:

[0079] {

[0080] source_content: Original 1500-word product introduction of an AI virtual try-on system

[0081] target_platforms: [platform_001, platform_002, platform_003],

[0082] task_type: product_intro,

[0083] brand_tone: professional_credible,

[0084] keyword_set: [AI virtual try-on, 3D human body modeling, clothing recommendation],

[0085] user_requirement: Avoid exaggerated promotional statements.

[0086] }

[0087] S2. Construct a target AI platform preference fingerprint database and extract platform profiles;

[0088] The system pre-maintains a platform preference fingerprint database, which consists of multiple platform preference objects. Each platform preference object contains at least content style features, citation style features, and keyword density control features.

[0089] Preferably, for the first Each target platform, and its platform preference objects are denoted as:

[0090]

[0091] in:

[0092] Indicates the first The platform preference objects corresponding to each target AI platform;

[0093] This indicates the content style characteristics and is used to limit the way the output content is expressed. The preferred values ​​include one or more of professional, concise, structured, and natural.

[0094] This indicates the citation style feature, which limits the citation method used for factual statements in the output. Preferred values ​​include one or more of inline, footnote, source_links, and academic.

[0095] This is the keyword density threshold, a preset floating-point value used to limit the maximum allowed density of keywords in the output text;

[0096] As an evidence strength feature, it is used to limit the source support strength of the output content, so as to characterize the target platform's requirements for traceable sources, factual support or completeness of citations. For example, it can be divided into 1 to 3 levels according to the strictness. Level 1 only requires logical consistency and does not require external evidence. Level 2 requires authoritative sources to accompany core facts. Level 3 requires verifiable sources to accompany all facts.

[0097] This is a length control feature used to limit the length range of the output text. The storage format is a pair of token interval values, such as [1000, 2000], which means that the number of output tokens should be controlled between 1000 and 2000.

[0098] This is a structural preference feature used to define how heading levels, bullet points, summary blocks, or FAQ blocks are organized.

[0099] One of the innovative aspects of this invention is the creation of an independent platform preference object and the transformation of platform preferences from hard-coded Prompt strings into parameterized feature sets. Instead of hard-coding Prompt strings, the system maintains a map[string]models.AIPreference mapping, abstracting platform preferences into feature domain vectors such as ContentStyle, CitationStyle, and KeywordDensity.

[0100] As a further preferred implementation, the platform preference fingerprint database can be stored in a Redis or MySQL database, with the key being a unique platform identifier and the value being a serialized preference object. Specific example values ​​are as follows: the preference object for the conversational search platform is... For simplicity, For source link citation, It is 0.015. Level 2 The range is [500, 1000]. It consists of short paragraphs, numbered bullet points, and a summary in the first paragraph.

[0101] like Figure 2As shown, in this embodiment, the system pre-maintains a platform preference fingerprint database, using a key-value mapping structure to store the preference features corresponding to all target AI platforms. The system extracts the preference objects for the corresponding platform based on the parsed target AI platform identifier as an index.

[0102] This step addresses the issue that existing technologies typically write platform rules directly into fixed prompt word templates. When platform rules are updated, all associated templates need to be modified, resulting in high maintenance costs. This solution abstracts platform rules into independently updatable feature parameters. Rule iteration can be completed simply by adjusting the parameter values, without modifying the prompt word generation logic, thus significantly improving rule iteration efficiency.

[0103] S3. Implement multi-dimensional constraint fusion and priority arbitration;

[0104] The brand tone, task type, structural strategy and keyword set obtained in step S1 are merged with the platform preference object extracted in step S2 to obtain a unified constraint set.

[0105] Preferably, the unified constraint set is denoted as:

[0106]

[0107] in The unified set of constraints after fusion corresponds one-to-one with the feature dimensions of the platform's preferred objects, namely the final style constraint, the final citation constraint, the final keyword density constraint, the final evidence constraint, the final length constraint, and the final structure constraint.

[0108] Preferably, various constraints are arbitrated according to a preset priority to obtain the final constraint value:

[0109]

[0110] in: Indicates the first The final arbitration result of class constraints, It can represent any dimension of style, citation, density, evidence, length, or structure; Indicates the target platform for the first Class constraint requirements; Indicates the brand tone for the first Class constraint requirements; Indicates the task type for the first... Class constraint requirements; This indicates that the general generation rule or compliance rule applies to the first... Class constraint requirements;

[0111] This represents the priority selection function, which is used to output the final constraint results according to the preset priority.

[0112] Preferably, the priorities from highest to lowest are: citation and evidence constraints, platform structure constraints, task objective constraints, brand tone constraints, and style fine-tuning constraints.

[0113] As a further preferred implementation, the constraint priority weighting is configured as follows: citation and evidence constraints have the highest weight of 10, structural constraints have a weight of 8, task type constraints have a weight of 6, brand tone constraints have a weight of 4, and style fine-tuning constraints have a weight of 2. The specific conflict handling rules are as follows: if platform preferences and brand tone conflict, the rigid platform constraints are retained first, and non-rigid style constraints are compromised and integrated. For example: if a platform requires citations to be footnotes, while the brand tone requires citations to be inline, the citation constraint has the highest weight, and the platform-required footnote is ultimately chosen; if the platform requires a professional style, while the brand requires a lively style, the style constraint has the lowest weight, and the compromise value of professional and easy-to-understand is ultimately chosen. If the generated request includes multiple target AI platforms, the system does not merge the constraints of different platforms, but directly generates multiple independent unified constraint sets, which are then processed separately in subsequent steps.

[0114] When multiple target platforms exist simultaneously, instead of directly merging all platform constraints, multiple branch constraint sets can be generated for different platforms and then proceeded to subsequent steps respectively.

[0115] like Figure 3 As shown, in this embodiment, the system will perform conflict detection with the extracted platform preference object, the parsed brand tone, task type, structural strategy, and preset general generation rules, and arbitrate according to preset priority to generate a unified constraint set.

[0116] This step addresses the fact that existing technologies typically only consider single-dimensional constraints and cannot automatically handle conflicts arising from multiple sources. For example, if a platform requires a keyword density of no more than 1.5%, while operations require a keyword density of no less than 2%, such conflicts require manual adjustments one by one, which is inefficient and prone to errors. The priority arbitration mechanism in this solution can automatically resolve more than 95% of common conflict scenarios without manual intervention.

[0117] S4. Construct system-level guidelines based on a unified set of constraints;

[0118] Based on the unified constraint set obtained in step S3 Automatically generate corresponding system-level instructions This step addresses the issue that existing technologies use fixed templates for prompts, only replacing keyword variables, which cannot adapt to dynamically changing constraint combinations. This solution's dynamic construction logic can generate suitable prompts based on any constraint combination, eliminating the need for manually writing multiple templates and significantly improving prompt generation efficiency.

[0119] Preferably, system-level instructions It can be represented as:

[0120]

[0121] in: This indicates a system-level instruction. This section describes the task intent, explaining the core objective of the generated task. It is automatically generated based on the task type. For example, the task intent for a product introduction is to generate product introduction content that meets the requirements based on given source materials. Represents a unified set of constraints; Represents a set of keywords; This indicates the user's original supplementary request; This indicates a prompt word construction function, which is used to weave task intent, constraint rules, keyword requirements, and user supplementary information into system-level instructions according to a preset template.

[0122] Preferably, the system-level instructions include at least the following: expression style constraints, citation method constraints, keyword density constraints, structural format constraints, evidence integrity constraints, and output type constraints.

[0123] For example, when the target platform prefers concise expressions and external links, the system-level guidelines will automatically include restrictions such as "use concise expressions", "accompany factual statements with external source links or footnotes", and "keyword density does not exceed the preset threshold".

[0124] As a further preferred implementation, the Build function is implemented as follows: First, the task intent description is concatenated. Then, the six dimensions of the unified constraint set are transformed into explicit rule entries one by one. The rule entries adopt a format of requirements plus specific numerical values ​​or enumeration values ​​to avoid vague expressions. Next, keyword density requirements are concatenated, and finally, user-supplemented requirements are concatenated. A complete system-level instruction example for a conversational search platform is as follows:

[0125] You now need to generate product introduction content for an AI virtual try-on system for a conversational search platform, with the following requirements: 1. Concise style, conclusion first, avoid redundant descriptions; 2. All core factual statements must be accompanied by traceable official source links, marked as footnotes; 3. The density of keywords AI virtual try-on, 3D human body modeling, and clothing recommendations should not exceed 1.5%; 4. Short paragraphs, each no more than 3 lines, with key points presented using numerical bullet points, and the opening should prioritize a core conclusion of no more than 50 words; 5. Avoid exaggerated promotional statements and maintain objectivity and credibility. The generated content should be based on the following source material: [Insert 1500-word original AI virtual try-on system product introduction text here].

[0126] To cater to the different instruction compliance preferences of various basic large models, the wording of the rules can be adjusted. For example, imperative sentences can be used for GPT series models, while more explicit prohibitive statements can be used for open-source large models, thereby improving instruction compliance rates.

[0127] S5. Input the system-level instructions and source content into the generation model to obtain the target platform adaptation results;

[0128] Considering that existing technologies typically concatenate fixed prompts with source content before inputting them into the model, resulting in poor adaptability, this solution provides input prompts that correspond one-to-one with the target platform. Platform adaptation can be achieved without modifying the model's core parameters, reducing the adaptation cost for large models. This embodiment uses the system-level instructions generated in step S4. The source content from step S1 is input into the basic generation model to obtain the target platform adaptation result.

[0129] Preferably, the output result is denoted as:

[0130]

[0131] in: Indicates facing the first The results generated by the target AI platform; Indicates source content; Indicates facing the first System-level guidelines for building a target AI platform; This represents the reasoning process of the basic generative model.

[0132] Generating content by calling a basic large language model is a common reasoning process in existing generative artificial intelligence systems; however, the innovation of this invention lies in... It is not a fixed template, but is automatically generated based on platform preference profiles and multi-dimensional constraints in the arbitration process.

[0133] As a further preferred implementation, the base generative model can be a general-purpose large language model, including GPT-4o, Claude 3 Opus, or the open-source Llama 3 70B, without the need for additional full training. The fixed parameters during invocation are set to a temperature value of 0.3 and a top_p value of 0.8. The maximum output token count is increased by 100 tokens of redundancy based on the upper limit of the length constraint. For example, when the length constraint is 500 to 1000 tokens, the maximum output token count is set to 1100, and the output format is plain text. If higher platform-adaptive accuracy is required, a lightweight fine-tuning approach can be used to optimize the model. The fine-tuning dataset consists of publicly available content with high platform adaptability, with a sample size of 1000 to 2000. Fine-tuning is performed in 3 to 5 rounds, with a learning rate set to 2e-5. Only the embedding and output layers of the model are fine-tuned; the core network structure does not need to be modified.

[0134] S6. Calculate keyword density and perform result validation;

[0135] The frequency of keywords in the generated results is checked to ensure that the output meets the keyword density requirements of the target platform.

[0136] Considering that existing technologies typically only perform general compliance checks and do not conduct targeted checks based on platform preferences, it is easy for content to be demoted for not complying with platform rules. This solution's three-dimensional checks fully match the core dimensions of platform preferences, which can significantly improve the compliance rate of content with platform rules.

[0137] Preferably, for keywords Its output text Keyword density is defined as follows:

[0138]

[0139] in:

[0140] Keywords In the text Keyword density in; Keywords In the text The number of times it appears in; Representing text Total word count, total token count, or total measurable character count; when If the output does not meet the keyword density constraints of the target platform's preferences, it is considered that rewriting, compression, or replacement is required.

[0141] Preferably, step S6 further includes checking the reference integrity, structural integrity, and style consistency of the output results.

[0142] As a further preferred implementation method, the specific execution rules for the three verifications are as follows:

[0143] Keyword density verification: Calculate the density of all specified keywords. If the density of any keyword exceeds the platform's threshold, a correction strategy is triggered, including keyword replacement, synonym replacement, or partial rewriting. The density is then recalculated until it meets the requirements. A specific example is as follows: A generated result has a total of 1000 tokens. The keyword "AI try-on" appears 18 times, with a density of 0.018. The platform threshold is 0.015. Replacing "AI try-on" with "intelligent try-on system" 3 times reduces the density to 0.015, which meets the requirements.

[0144] Citation integrity check: Check all sentences containing numbers and concluding statements to see if they contain source identifiers that match the citation style. For example, if footnote citations are required, check if there is a [^1] class tag. If it is missing, mark the corresponding sentence and call the large model to supplement the source information.

[0145] Structure consistency check: Checks whether the output structure meets the structural constraints. For example, if a three-level heading is required, it checks whether the heading level matches, whether the abstract block is located in the first 10% of the full text, and whether the punctuation format meets the requirements. If not, it calls the structure adjustment function to reorganize the content according to the requirements.

[0146] S7, performs concurrent heterogeneous distribution across multiple platforms;

[0147] When the target AI platform identifier includes multiple platforms, the system automatically splits the original generation request into multiple branch requests, and executes steps S2 to S6 respectively according to different platform preferences to obtain multiple platform adaptation results.

[0148] Preferably, the branch request set is denoted as:

[0149]

[0150] in: Represents a set of branch requests; Indicates the first One branch request; Indicates the number of target platforms.

[0151] The corresponding set of generated results is denoted as:

[0152]

[0153] in: This represents a collection of results generated across multiple platforms. Indicates the first The generated results for each target platform.

[0154] like Figure 4 As shown, when the generated request contains multiple target AI platform identifiers, the system in this embodiment copies the original request into multiple independent branch requests, loads the corresponding platform's preference object for each branch request, independently executes the entire process from platform profile extraction to result verification, and generates differentiated results corresponding to each target AI platform.

[0155] This step takes into account that existing technologies usually adopt a serial generation method, which requires multiple submissions for multi-platform adaptation, resulting in low efficiency. The concurrent branching mechanism of this solution can generate content for multiple platforms simultaneously, and the improvement in generation efficiency is proportional to the number of target platforms.

[0156] As a further preferred implementation, concurrent scheduling employs Celery or Kubernetes Job scheduling mechanisms, with each branch request being an independent task instance that does not interfere with others. Each generated result is bound to standardized attribute fields, including a platform unique identifier, generation timestamp, structure type enumeration value, reference pattern enumeration value, and verification result flag, specifically platform_002, 202405201430, short_text, footnote, and pass, facilitating subsequent distribution, caching, tracing, and re-editing.

[0157] S8. Output multimodal results.

[0158] After the main text result is generated, the system in this embodiment generates a set of supporting multimodal results based on the same platform preference object and unified constraint set, including title candidate set, summary card, FAQ block, cited evidence block, illustration description text, and image generation prompt.

[0159] This step takes into account that existing multimodal content is usually generated separately, which can easily lead to inconsistencies in style and information. The multimodal content in this solution shares the same set of constraints, which can ensure that the style, structure and information expression of all modal content are completely consistent.

[0160] Preferably, after obtaining the main text result, the system further generates title candidates, summary cards, FAQ blocks, evidence blocks, illustration descriptions or image generation prompts according to the same target platform preferences, so as to form a multimodal content package for the target platform.

[0161] The multimodal results share the same platform preference object and unified constraint set with the main text results, thereby ensuring consistency in style, structure and citation methods among different modalities.

[0162] As a further preferred implementation, the generation rules for each multimodal content are as follows: 3 to 5 candidate titles are generated, with the length meeting platform requirements; the summary card length is controlled within 100 characters, covering the core conclusions; the FAQ block extracts the Top 10 frequently asked user questions from the main text result, with each answer controlled within 100 characters; image generation prompts are adapted to the Stable Diffusion or MidJourney model, with a style consistent with the main text result. A specific example is a professional and technological style, an AI virtual fitting system interface, a white background, simple and without redundant elements, with a resolution of 1920*1080.

[0163] Secondly, corresponding to the above methods, embodiments of the present invention also provide a dynamic prompt word adaptation and multimodal generation system based on a target AI platform profile, comprising:

[0164] The request receiving and parsing module is used to receive the source content to be optimized, the target AI platform identifier, the task type, the brand tone, the structural strategy, and the keyword set;

[0165] The platform preference fingerprint module is used to store and output platform preference objects corresponding to the target AI platform;

[0166] The constraint fusion and arbitration module is used to prioritize and arbitrate platform preferences, brand tone, task types, and general rules to generate a unified set of constraints.

[0167] The dynamic prompt word construction module is used to generate system-level instructions based on a unified set of constraints;

[0168] The generation execution module is used to call the basic generation model and generate adaptation results based on system-level instructions and source content;

[0169] The result verification module is used to verify keyword density, citation integrity, structural integrity, and style consistency.

[0170] The concurrent distribution module is used to split requests into multiple branch tasks and output multiple platform-adapted results when multiple target AI platforms coexist.

[0171] The multimodal output module generates titles, summaries, FAQs, quotation blocks, and image prompts consistent with the main text results.

[0172] Compared with the prior art, the beneficial effects of the present invention are reflected in:

[0173] 1. Achieving parametric modeling of platform preferences. This invention transforms the preferences of different AI platforms from uninterpretable empirical rules into computable, callable, and scalable platform preference objects, solving the problems of existing systems relying on hard-coded Prompt strings, difficulty in reuse, and difficulty in maintenance.

[0174] 2. Achieving dynamic arbitration and weaving of multiple constraints. This invention does not simply superimpose platform preferences, brand tone, and task objectives, but instead obtains a unified set of constraints through a priority arbitration function, and then constructs system-level instructions. Therefore, it can significantly improve the consistency of output results in terms of structure, tone, evidence, and keyword density.

[0175] 3. Significantly improves content production efficiency across multiple platforms. When facing multiple target AI platforms, this invention can automatically clone branch requests and generate content results corresponding to multiple platforms, eliminating the need for manual writing of multiple versions of prompts and copy, thus significantly reducing the labor costs of content production and distribution.

[0176] 4. Improves adaptability and citationability to target platforms. Because this invention can automatically adjust the output content according to the target platform's requirements for structure, evidence, and citation format, it is more conducive to improving the parsing efficiency, display matching degree, and citation probability of content on the target AI platform.

[0177] 5. Supports consistent multimodal output. This invention can not only generate the main text result, but also generate summary cards, FAQ blocks, title sets, evidence blocks, and image prompts under the same constraint system, so that the multimodal output maintains consistency in style, structure, and source expression.

[0178] Example 1

[0179] Taking the adaptation of a company's product copy to multiple target AI platforms as an example, the implementation steps are as follows:

[0180] S1, Receive raw input data.

[0181] Suppose a company needs to publish a product description for an "AI virtual fitting system," and a user enters the following information into the system:

[0182] Source content A product introduction document of approximately 1500 words, covering 3D human body modeling, clothing matching, recommendations, and application scenarios;

[0183] Target AI Platform Collection ;

[0184] Brand tone : Technological, credible, restrained, and not exaggerated;

[0185] Task type Main text generation + summary card + FAQ generation;

[0186] Keyword set .

[0187] In this step, receiving the original text and basic parameters is a standard practice in existing content generation systems; however, incorporating the target AI platform identifier, brand tone, task type, and keyword set into a unified structured request object is a step of this invention that serves subsequent profile matching and constraint arbitration.

[0188] S2. Extract platform profiles from the platform preference fingerprint database.

[0189] The system reads the target platform set sequentially. The system identifies each platform and calls the platform preference extraction process. Let:

[0190]

[0191]

[0192]

[0193] Preferably, the following settings can be configured:

[0194] For ChatGPT: , , ;

[0195] Regarding Perplexity: , , ;

[0196] Regarding Google AI Overviews: , , .

[0197] Its core is not simply selecting a string, but selecting a set of feature domain constraints.

[0198] S3, Enforcement Constraint Integration and Conflict Arbitration.

[0199] For the ChatGPT branch, the brand tone should be "restrained and not exaggerated," the platform preference should be "professional and lengthy," and the task type should include "main text + abstract + FAQ." The system calculates a unified set of constraints:

[0200]

[0201] Preferably, the arbitration result is as follows:

[0202] Professional, rational, and not overly marketing;

[0203] Inline interpretive citation;

[0204] Medium to long article;

[0205] Hierarchical headings + conclusion introductory paragraph + FAQ block.

[0206] For the Perplexity branch, the platform preferred "conciseness, inclusion of source links, and low keyword density," a "credible and restrained" brand tone, and a task type requirement of "body text + summary card." The arbitration result was:

[0207] Concise and to the point;

[0208] External source links or footnotes;

[0209] ;

[0210] Short paragraphs, strong bullet points, and priority given to abstracts.

[0211] For the Google AI Overviews branch, the platform prefers "strong structure and clear credible endorsement," and the arbitration result is as follows:

[0212] Structured and clearly explained;

[0213] Academic or evidence-block style of source expression;

[0214] The block consists of a core conclusion block, a key points block, and a FAQ block.

[0215] In cases where there is a conflict between the brand's tone and the platform's preferences—for example, if the company's brand style is more casual while the platform requires a professional approach—the system will not simply override the platform's requirements. Instead, it will prioritize maintaining the platform's rigid requirements and then use the brand's tone to soften the message appropriately. This mechanism is the innovation of this invention.

[0216] S4. Construct dynamic system-level guidelines.

[0217] The systems are based on , and Develop three sets of system-level guidelines.

[0218] For example, for the Perplexity branch, the following structured instructions can be generated:

[0219] Answer in the simplest way possible;

[0220] All factual statements must be accompanied by traceable sources;

[0221] The density of keywords "AI virtual fitting", "3D human body modeling", and "clothing recommendation" must not exceed the preset limit;

[0222] Prioritize outputting conclusions and key points;

[0223] Avoid redundant descriptions and exaggerated promotional expressions.

[0224] S5. Call the generated model to output customized results from the platform.

[0225] The system executes the following respectively:

[0226]

[0227]

[0228]

[0229] in:

[0230] Indicates instructions for ChatGPT;

[0231] Indicates instructions for Perplexity;

[0232] This refers to instructions for Google AI Overviews.

[0233] , , These represent the generated results for the three target platforms, respectively.

[0234] The result after execution is:

[0235] A longer, more detailed discussion for ChatGPT;

[0236] A concise and efficient source tracing version for Perplexity;

[0237] A structured summary for Google AI Overviews.

[0238] S6. Perform keyword density and citation integrity checks.

[0239] Taking the Perplexity branch output as an example, the system calculates the density of the keyword "AI try-on":

[0240]

[0241] If the calculation result is greater than 0.015, the system will trigger an automatic rewriting strategy to reduce duplicate keywords or replace synonyms.

[0242] At the same time, the system checks whether factual statements in the text are accompanied by source identifiers. If they are missing, it triggers a process to supplement the source block or a local regeneration process.

[0243] In this step, simple text quality verification is a routine post-processing operation in existing natural language generation systems; however, verification based on specific density thresholds and source expression methods in the target platform preferences is a specific post-processing strategy of this invention for platform profile adaptation.

[0244] S7, executes concurrent heterogeneous distribution across multiple platforms.

[0245] The system automatically copies the original request into three branch requests:

[0246]

[0247] Three sets of results were obtained respectively:

[0248]

[0249] The system then binds the target platform identifier and distribution tag to each result and outputs it to the result management module.

[0250] For example:

[0251] Link to "Long Explanation Version";

[0252] Bind to the "Source Traceability Version";

[0253] Bind to "Structure Summary Version".

[0254] S8. Output multimodal results.

[0255] After generating the above three main text results, the system further generates profiles based on their respective platforms:

[0256] Three candidate titles;

[0257] One set of summary cards;

[0258] FAQ (Question and Answer Block 1)

[0259] Image generation prompts (set 1).

[0260] All additional results share the same set of constraints as the corresponding main text results.

[0261] For example, the FAQs generated by the Perplexity branch are shorter and emphasize facts and sources; the summary cards generated by the Google branch are more structured and focused on key points; and the ChatGPT branch generates extended questions and answers that are more suitable for the context of longer texts.

[0262] Through the above steps, this embodiment achieves differentiated adaptation output of the same enterprise source copy on multiple target AI platforms, proving that the present invention can effectively solve the problem that the unified template generation in the prior art cannot be finely adapted to different platform preferences.

[0263] Example 2: System Example

[0264] This embodiment provides a dynamic prompt word adaptation and multimodal generation system based on a target AI platform profile. This system is used to implement the method of Embodiment 1. The system includes:

[0265] The request receiving and parsing module is used to receive source content, target platform identifier, brand tone, task type, structure strategy and keyword set, and organize them into a unified request object;

[0266] The platform preference fingerprint module is used to return the corresponding platform preference object based on the target platform identifier. ;

[0267] The constraint fusion and arbitration module is used to fuse platform preferences, brand tone, task requirements, and general rules according to preset priorities, and output a unified constraint set. ;

[0268] The dynamic prompt word construction module is used to construct prompt words based on a unified set of constraints. Task Intent Keyword set Supplementary requirements for users Build system-level guidelines ;

[0269] The generation execution module is used to call the basic generation model for execution.

[0270]

[0271] The result verification module is used to detect keyword density, source integrity, structural integrity, and style consistency.

[0272] The concurrent distribution module is used to automatically clone a request into multiple branch requests and generate corresponding results for each when there are multiple target platforms.

[0273] The multimodal output module is used to generate additional results such as titles, summaries, FAQs, quotation blocks, and image prompts.

[0274] The system architecture in this embodiment can be implemented using a server deployment method, a cloud service method, or a local integrated machine method. Data interaction between modules via interface calls or message queues is an optional implementation method of this invention.

[0275] Based on the same inventive concept, this invention also provides a computer device, comprising: one or more processors, and a memory for storing one or more computer programs; the programs include program instructions, and the processor executes the program instructions stored in the memory. The processor may be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. It is the computing and control core of the terminal, used to implement one or more instructions, specifically for loading and executing one or more instructions stored in a computer storage medium to implement the above-described method.

[0276] It should be further explained that, based on the same inventive concept, the present invention also provides a computer storage medium storing a computer program, which, when executed by a processor, performs the above-described method. This storage medium can be any combination of one or more computer-readable media. A computer-readable medium can be a computer-readable signal medium or a computer-readable storage medium. A computer-readable storage medium can be, for example, but not limited to, an electrical, magnetic, optical, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of computer-readable storage media (a non-exhaustive list) include: an electrical connection having one or more wires, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof. In the present invention, a computer-readable storage medium can be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device.

[0277] It should be noted that, unless otherwise defined, the technical or scientific terms used in this invention should have the ordinary meaning understood by one of ordinary skill in the art to which this invention pertains. The terms "first," "second," and similar terms used in this invention do not indicate any order, quantity, or importance, but are merely used to distinguish different components. Terms such as "comprising" or "including" mean that the element or object preceding the word encompasses the elements or objects listed following the word and their equivalents, without excluding other elements or objects. Terms such as "connected" or "linked" are not limited to physical or mechanical connections, but can include electrical connections, whether direct or indirect. Terms such as "upper," "lower," "left," and "right" are used only to indicate relative positional relationships; when the absolute position of the described object changes, the relative positional relationship may also change accordingly.

[0278] This invention is not limited to the above-described preferred embodiments. Anyone inspired by this invention can derive other forms of dynamic prompt word adaptation methods based on target AI platform profiles. All equivalent variations and modifications made within the scope of the claims of this invention should be included in the scope of this invention.

Claims

1. A method for dynamic prompt word adaptation based on target AI platform profiles, characterized in that, Includes the following steps: Receive a generation request and parse it to obtain the source content, the target AI platform identifier, and at least one additional generation constraint; Based on the target AI platform identifier, the corresponding platform preference object is extracted from the preset platform preference fingerprint database; the platform preference object is a structured data set used to parameterize the multi-dimensional output preferences of the target AI platform for the generated content. The platform preference object is subjected to conflict detection with the additional generation constraints and the preset general generation rules, and arbitration is performed according to the preset priority rules to generate a unified constraint set. Based on the unified constraint set, system-level instructions adapted to the target AI platform are dynamically generated. The system-level instructions and the source content are input into the generation model to obtain text generation results adapted to the target AI platform.

2. The method for dynamic prompt word adaptation based on target AI platform profile as described in claim 1, characterized in that: The platform preference objects include at least content style features, citation style features, keyword density threshold, evidence strength features, structural preference features, and length control features.

3. The dynamic prompt word adaptation method based on target AI platform profile as described in claim 1, characterized in that: The preset priority rules are sorted from highest to lowest constraint priority as follows: citation and evidence constraints, structural constraints, task type constraints, brand tone constraints, and style fine-tuning constraints.

4. The dynamic prompt word adaptation method based on target AI platform profile as described in claim 1, characterized in that: The system-level instructions are generated by a dynamic prompt word builder. The system-level instructions include at least the constraints on expression style, citation method, keyword density, structural format, evidence integrity, and output type.

5. The dynamic prompt word adaptation method based on target AI platform profile according to claim 1, characterized in that: After obtaining the text generation result adapted to the target AI platform, an adaptation check is performed on the text generation result, and if the check fails, rewriting, partial regeneration, or full regeneration is performed; the adaptation check includes keyword density check, citation integrity check, and structural consistency check.

6. The method for dynamic prompt word adaptation based on target AI platform profile according to claim 1, characterized in that: When the target AI platform identifier in the generation request includes multiple target AI platforms, the generation request is copied into multiple branch requests corresponding to the number of target AI platforms. For each branch request, all steps from extracting platform preference objects to generating text generation results are executed to generate differentiated results that correspond one-to-one with each target AI platform, and each differentiated result is bound to the corresponding target AI platform identifier.

7. The dynamic prompt word adaptation method based on target AI platform profile as described in claim 6, characterized in that: Based on the text generation result or the differentiation result, a multimodal result adapted to the corresponding target AI platform is generated; the multimodal result and the corresponding text generation result or differentiation result share the same platform preference object and unified constraint set.

8. A dynamic prompt word adaptation system based on target AI platform profiles, characterized in that, include: The request receiving and parsing module is used to receive generation requests and parse them to obtain the source content, the target AI platform identifier, and at least one additional generation constraint. The platform preference fingerprint module is used to pre-store structured platform preference objects corresponding to multiple target AI platforms, and output the corresponding platform preference objects according to the target AI platform identifier; the platform preference objects are structured data sets used to parameterize the multi-dimensional output preferences of the target AI platform for the generated content; The constraint fusion and arbitration module is used to perform conflict detection between the platform preference object and the additional generation constraints and the preset general generation rules, and to perform arbitration according to the preset priority rules to generate a unified constraint set. The dynamic prompt word construction module is used to dynamically generate system-level instructions adapted to the target AI platform based on the unified constraint set; The generation execution module is used to input the system-level instructions and the source content into the generation model to obtain text generation results adapted to the target AI platform.

9. A dynamic prompt word adaptation system based on a target AI platform profile as described in claim 8, characterized in that, It also includes a result verification module, which is used to perform adaptability verification on the text generation result, and to perform rewriting, partial regeneration or full regeneration when the verification fails; the adaptability verification includes keyword density verification, citation integrity verification and structural consistency verification.

10. A dynamic prompt word adaptation system based on a target AI platform profile as described in claim 8, characterized in that, It also includes a concurrent distribution module and a multimodal output module; the concurrent distribution module is used to copy the generation request into multiple branch requests corresponding to the number of target AI platforms when the target AI platform identifier in the generation request includes multiple target AI platforms, and schedule each module to generate differentiated results corresponding to each target AI platform; the multimodal output module is used to generate multimodal results adapted to the corresponding target AI platform based on the text generation result or the differentiated result.

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