Contextual Prompt Layer for LLM Service Integration
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Solution Overview
Problem
Large language models (LLMs) are generic and stateless, making them inefficient when integrated into specific applications and user interfaces, leading to wastage of computing resources due to inaccurate or offensive content generation and limited natural conversational capabilities.
Innovation Solution
Providing sophisticated contextual prompts to LLMs with application, user, and platform information to generate relevant responses, reducing the need for trial and error and improving user interaction by preserving computing resources.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If LLMs are integrated into specific applications without fine-tuning, then the system maintains simplicity and ease of deployment, but the model generates inaccurate or irrelevant content and wastes computing resources
Solution Approach 1:
The patent introduces a prompt engineering layer as an intermediary between the generic LLM and the specific application domain. This layer transforms user inputs into context-rich prompts that include domain-specific knowledge, task instructions, and relevant parameters, enabling the LLM to generate accurate content without requiring fine-tuning or retraining of the model itself.
Solution Approach 2:
The system performs preliminary actions by pre-configuring prompt templates that embed domain knowledge, context information, and task-specific instructions before the LLM processes user requests. This preparation work is done once during system setup rather than repeatedly during operation, improving both deployment simplicity and content accuracy.
2Device complexity
If LLMs are used without contextual prompts, then the system architecture remains simple, but computing resources are wasted due to trial and error interactions
Solution Approach 1:
The patent implements preliminary action by pre-defining prompt templates that contain embedded context, domain knowledge, and task instructions. These templates are prepared in advance and automatically populated with user-specific information, eliminating the need for multiple trial-and-error interactions and reducing computing resource wastage while maintaining architectural simplicity.
Solution Approach 2:
The system changes the parameter of prompt quality by transforming simple user inputs into enriched prompts that include contextual parameters such as domain knowledge, task type, relevant entities, and formatting requirements. This parameter enhancement enables the LLM to generate accurate responses in fewer attempts, reducing energy loss.
3Loss of time
If generic LLMs are deployed for specific tasks, then deployment is quick and easy, but the models lack domain-specific knowledge and conversational capabilities
Solution Approach 1:
The patent segments the knowledge and context requirements from the LLM model itself, placing domain-specific knowledge into separate prompt templates. This segmentation allows the generic LLM to be deployed quickly while adapting to specific domains through the modular prompt structures that can be independently configured for different applications.
Solution Approach 2:
The patent creates a universal prompt engineering framework that can be applied across multiple domains and applications. The same generic LLM can serve different specific tasks by switching between different prompt templates, achieving adaptability without requiring domain-specific model instances for each application.
Data Source
AI summary
An example embodiment may include: receiving, from an application, a request, wherein the request includes textual content; in response to receiving the request, determining a context relating to the textual content; generating, from the textual content and the context, a prompt for a natural language model; transmitting, to the natural language model, the prompt; receiving, from the natural language model, a response to the prompt, wherein the response includes information relevant to the request or programmatic commands; and providing, to the application, further textual content that is based on the response.