agentv-prompt-optimizer

To automatically optimize prompts against evaluation datasets, you need a tool that iteratively executes evaluations, analyzes failures, and applies atomic operations like ADD, UPDATE, or DELETE to refine instructions. This reduces manual prompt engineering effort by adjusting prompts based on evaluation results.

AI-driven iterative refinement improves prompt performance by analyzing evaluation failures to identify root causes and suggesting specific improvements. It continuously adjusts instructions through subagents that evaluate results and refine prompts until desired accuracy and effectiveness are met.

Yes, you can infer prompt files directly from existing LLM evaluation content. The optimization process analyzes your evaluation datasets to automatically infer the initial prompt files, which are then iteratively refined against those test cases.

The best way to analyze root causes of prompt failures in LLMs is using an iterative evaluation tool that executes tests, analyzes the specific failures, and refines instructions through targeted atomic operations to resolve the identified root causes.

You need an evaluation dataset, such as a YAML file, to automatically refine instructions. The optimization process uses this evaluation content to infer prompt files, execute evaluations, and iteratively apply updates to improve prompt performance against the test cases.