AI full-link management and accurate generation system and implementation method

Abstract

The application discloses an AI full-link management and accurate generation system and an implementation method, and belongs to the technical field of artificial intelligence interaction. The application adopts a closed-loop architecture of three-end linkage of user-end question standardization, connection-end memory optimization and AI-end output constraint, and can be compatible with mainstream AI interfaces as lightweight middleware without changing the underlying large model. Through three major core innovations of double-layer intent recognition, double-end external long-term memory, lazy coefficient and precision threshold double-quantization control, the application solves the pain points of existing large models, such as understanding deviation, uncontrollable memory, output distortion, compliance risk and the like, realizes full-link standardization, precision and compliance management of AI interaction, and is suitable for natural language question and answer, creative content generation, professional knowledge consultation and other multi-scene AI landing applications.

Description

Technical Field

[0001] This invention relates to the field of artificial intelligence interaction technology, specifically to an AI end-to-end governance and accurate generation system and implementation method for multiple scenarios, applicable to AI application scenarios such as natural language question answering, AI image generation, professional knowledge consulting, and creative content creation, and belongs to the category of artificial intelligence middleware technology. Background Technology

[0002] With the rapid development of Large Language Model (LLM) technology, AI interaction has been widely applied in various fields such as office work, creation, and consultation. However, existing large models still have several core defects, which seriously affect user experience and feasibility of implementation: 1. Comprehension bias: User natural language questions may contain ambiguity, incomplete information, or unclear intent, which can easily lead to misjudgment of user needs by large models, resulting in irrelevant answers. 2. Uncontrollable memory problem: The context window of existing large models is limited, long-term memory is easy to be lost and confused, and the memory is stored inside the model, which users cannot manage, update and recall independently; 3. Output distortion issues: Large models suffer from problems such as illusions, redundant output, and non-standard formats, which cannot meet the accuracy and standardization requirements of professional scenarios; 4. Compliance risk issues: The content generated by the large model may contain sensitive information or illegal content, and there is a lack of a full-chain compliance control mechanism.

[0003] Existing technologies mostly optimize single defects, such as only optimizing the memory module or only performing output verification. They have not formed a closed-loop governance solution covering the entire chain of "user questioning - memory retrieval - AI generation - output verification". They cannot fundamentally solve the pain points of AI interaction in multiple scenarios. Moreover, most solutions require modification of the underlying large model, resulting in poor compatibility, high deployment costs, and difficulty in adapting to mainstream AI interfaces. Summary of the Invention

[0004] (a) Purpose of the invention To address the shortcomings of existing large-scale models, such as comprehension bias, uncontrollable memory, output distortion, high compliance risks, and the single-point optimization and poor compatibility of existing technologies, the present invention aims to provide an AI end-to-end governance and accurate generation system and implementation method. Through a lightweight middleware architecture with three-terminal linkage, without modifying the underlying large-scale model, it achieves standardized, accurate, and compliant governance across the entire chain, solves the core pain points of existing AI interaction, and adapts to the needs of AI implementation in multiple scenarios.

[0005] (II) Technical Solution This invention adopts a closed-loop architecture that links user-side question standardization, connection-side memory optimization, and AI-side output constraints, as detailed below: User-side question standardization module: Performs two-layer intent recognition, format standardization and compliance preprocessing on the original user questions, converts natural language questions into standardized prompts, eliminates ambiguity, filters risks, and ensures that the large model accurately understands user needs; Connection-end memory optimization module: Enables dual-end external storage, dynamic updates and accurate retrieval of users' long-term memory, breaks through the limitations of large model context windows, allows users to manage their memory independently, and avoids memory loss and confusion; AI-side output constraint module: Through dual quantitative control of laziness coefficient and accuracy threshold, combined with multiple rounds of output verification, it constrains the redundancy, accuracy and compliance of AI output, and achieves accurate, standardized and secure content generation; the three modules form a closed loop, serving as a lightweight middleware compatible with mainstream large model interfaces, and can achieve full-link governance without modifying the underlying model.

[0006] (III) Beneficial Effects End-to-end closed-loop governance: covering the entire process of user questioning, memory retrieval, AI generation, and output verification, fundamentally solving four core pain points: misunderstanding bias, memory loss, output distortion, and compliance risks; Lightweight and highly compatible: Deployed as middleware, it requires no modification to the underlying large model, is compatible with mainstream AI interfaces, has low deployment costs, and is suitable for a wide range of scenarios; Controllable and manageable memory: Dual-end external memory storage allows users to update and manage their memories independently, breaking through the limitations of context windows and enabling long-term accurate memory retrieval; Quantitative control and precise generation: By using two quantitative indicators, namely laziness coefficient and accuracy threshold, the output style and accuracy of AI are precisely controlled to meet the needs of different scenarios; End-to-end compliance assurance: From question preprocessing to output verification, the entire process is subject to compliance control, effectively avoiding risks associated with sensitive information and illegal content, and meeting regulatory requirements.

Claims

1. An AI end-to-end governance and precision generation system, characterized in that, include: The user-side question standardization module, the connection-side memory optimization module, and the AI-side output constraint module form a closed-loop linkage architecture. The user-side question standardization module is used to perform intent recognition, format standardization, and compliance preprocessing on the user's original questions; the connection-side memory optimization module is used to realize the external storage, dynamic updating, and accurate retrieval of the user's long-term memory; the AI-side output constraint module is used to constrain the AI ​​output through quantitative control rules to achieve accurate and compliant generation.

2. The system according to claim 1, characterized in that, The user-side question standardization module includes: a dual-layer intent recognition unit, used to accurately extract core user needs through rule matching and large model-assisted recognition dual verification; a format standardization unit, used to convert user natural language questions into standardized prompt templates; and a compliance preprocessing unit, used to perform sensitive word filtering, risk classification, and compliance verification on the questions.

3. The system according to claim 1, characterized in that, The connection-end memory optimization module includes: a dual-end external memory storage unit, used to store user personal information, historical interactions, preference settings and other memory data on both the user end and the server end; a memory dynamic update unit, used to update the memory bank in real time according to new user interactions, to achieve incremental maintenance of memory; and a precise memory retrieval unit, used to accurately match and retrieve relevant memory data according to the current question context, to avoid interference from irrelevant memories.

4. The system according to claim 1, characterized in that, The AI-side output constraint module includes: a dual-quantitative control unit for laziness coefficient and accuracy threshold, used to control the redundancy of AI output through laziness coefficient and constrain the accuracy of output through accuracy threshold; a multi-round output verification unit, used to perform fact-checking, compliance verification and format verification on AI-generated content; and an output format standardization unit, used to convert AI output into a standardized format that meets user needs.

5. A method for AI end-to-end governance and accurate generation, characterized in that, The system applied to any one of claims 1-4 includes the following steps: S1: The user end performs intent recognition, format standardization, and compliance preprocessing on the original question to generate a standardized prompt; S2: The connection end accurately calls the user's long-term memory data according to the current question and supplements it into the prompt; S3: The AI ​​end generates a compliant output based on the supplemented prompt through dual quantitative control of laziness coefficient and accuracy threshold; S4: The AI ​​end performs multiple rounds of verification on the generated content, outputs it to the user after passing the verification, and updates the connection end's memory bank at the same time.