Iterative LLM System Prompt Optimization for Service Response Quality
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Solution Overview
Problem
Existing system prompts for Large Language Models (LLMs) are complex to generate and often require manual optimization, leading to suboptimal responses, especially in specific applications like customer support chatbots, where predefined requirements are difficult to integrate effectively.
Innovation Solution
A method for automatically optimizing system prompts by iteratively submitting test user prompts to the LLM, evaluating responses based on quality rules, and updating the prompt until a desired performance threshold is met, using a combination of LLMs to enhance accuracy and efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If manual optimization of system prompts is used, then flexibility in crafting prompts is maintained, but time consumption and complexity increase significantly
Solution Approach 1:
The system enables automatic self-optimization of system prompts through iterative evaluation and generation processes. The LLM autonomously generates and refines prompts based on performance metrics without requiring continuous manual intervention, thereby reducing time consumption while maintaining optimization quality
Solution Approach 2:
The patent implements automated feedback loops where system prompts are evaluated against performance thresholds and user interactions. Evaluation metrics provide automatic feedback that triggers iterative prompt regeneration, creating a closed-loop optimization system that continuously improves performance without manual intervention
2Reliability
If predefined requirements are integrated into system prompts, then service accuracy is improved, but prompt complexity and difficulty of generation increase
Solution Approach 1:
The patent segments the system prompt generation process into distinct components: role definition, constraint specification, and requirement integration. This modular approach allows predefined requirements to be systematically incorporated without creating overly complex prompts, as each component can be optimized independently
Solution Approach 2:
The system dynamically adjusts prompt parameters such as tone, structure, and constraint levels based on service requirements and performance evaluation. By changing prompt parameters adaptively rather than using fixed complex templates, the system maintains high service accuracy while reducing prompt complexity
3Manufacturing precision
If iterative optimization of system prompts is implemented, then response quality is improved, but computational resources and processing time increase
Solution Approach 1:
The patent implements feedback-driven optimization where system prompts are evaluated against performance thresholds and automatically regenerated only when improvements are detected. This targeted iterative approach optimizes response quality while minimizing unnecessary computational resources by stopping optimization when performance targets are achieved
Solution Approach 2:
The system performs partial optimization iterations focused on specific prompt elements that most impact response quality, rather than completely regenerating prompts at every step. This selective iterative approach achieves improved response quality while reducing overall computational resource consumption
Data Source
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AI summary
A method and apparatus are proposed for determining a system prompt to be used when interacting with a LLM for delivering a given service to a user, comprising: - Obtaining a current system prompt (ISP, CSP) relative to a context of said service, - Submitting a plurality of test user prompts (TUP) to said LLM as input along with said system prompt and obtaining a corresponding plurality of generated answers as output, - Determining a global performance score (GSC) for the plurality of generated answers based on quality rules (QR); - While the global performance score is less than a given threshold (TH), updating (E6) the current system prompt based on the global performance score and said quality rules, obtaining an updated system prompt and reiterating the preceding operations with the updated system prompt.