autoresearch-prompt

You can optimize LLM prompts automatically by running an autonomous, evaluation-driven experiment loop that tests prompt variations against labeled datasets, analyzes failures, and iteratively refines the prompt to improve task accuracy.

Evaluation-driven prompt optimization works by executing your current prompt against a labeled dataset, analyzing the misclassified or failed extractions, and autonomously applying targeted edits to the prompt to improve its performance on subsequent iterations.

Yes, autonomous prompt refinement requires a Python environment configured with Anthropic API access to execute the iterative prompt edits, run evaluations, and compute scoring against predefined metrics.

Yes, automated prompt optimization supports both classification and extraction tasks by continuously testing your prompt against labeled examples and refining it until it achieves high accuracy on the target dataset.

The best way to fix an unreliable classification prompt is to use an autonomous research loop that repeatedly evaluates the prompt against your data, identifies failure patterns, and makes targeted edits to improve accuracy.

Automated prompt evaluation loops require predefined labeled datasets and structured metrics to function effectively, meaning they are limited to refining prompts for tasks where ground-truth examples and clear scoring criteria are available.