SI-Agent: An Agentic Framework for Feedback-Driven Generation and Tuning of Human-Readable System Instructions for Large Language Models

• URL: http://arxiv.org/abs/2507.03223v1
• Date: Thu, 03 Jul 2025 23:44:50 GMT
• Title: SI-Agent: An Agentic Framework for Feedback-Driven Generation and Tuning of Human-Readable System Instructions for Large Language Models
• Authors: Jeshwanth Challagundla,
• Abstract summary: System Instructions (SIs) are pivotal for guiding Large Language Models (LLMs)<n>Existing automated methods frequently generate non-human-readable "soft prompts," sacrificing interpretability.<n>This paper introduces SI-Agent, a novel agentic framework designed to automatically generate and iteratively refine human-readable SIs.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: System Instructions (SIs), or system prompts, are pivotal for guiding Large Language Models (LLMs) but manual crafting is resource-intensive and often suboptimal. Existing automated methods frequently generate non-human-readable "soft prompts," sacrificing interpretability. This paper introduces SI-Agent, a novel agentic framework designed to automatically generate and iteratively refine human-readable SIs through a feedback-driven loop. SI-Agent employs three collaborating agents: an Instructor Agent, an Instruction Follower Agent (target LLM), and a Feedback/Reward Agent evaluating task performance and optionally SI readability. The framework utilizes iterative cycles where feedback guides the Instructor's refinement strategy (e.g., LLM-based editing, evolutionary algorithms). We detail the framework's architecture, agent roles, the iterative refinement process, and contrast it with existing methods. We present experimental results validating SI-Agent's effectiveness, focusing on metrics for task performance, SI readability, and efficiency. Our findings indicate that SI-Agent generates effective, readable SIs, offering a favorable trade-off between performance and interpretability compared to baselines. Potential implications include democratizing LLM customization and enhancing model transparency. Challenges related to computational cost and feedback reliability are acknowledged.

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