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Reflective Prompt Tuning (RPT) automates prompt optimization by using LLM function calling to mimic human prompt engineering workflows. RPT leverages an LLM optimizer that iteratively calls a diagnostic function to evaluate the target model on an entire optimization set, summarize failure modes, and generate structured diagnostic reports. The optimizer then uses these reports and an accumulated memory of prior reports to revise the prompt, leading to improved performance and confidence calibration across reasoning tasks.
Forget painstakingly crafting prompts by hand – RPT uses LLM function calling to automate prompt optimization, achieving up to 12.9 point improvements on reasoning tasks and better confidence calibration.
Large language models (LLMs) have become increasingly capable of following instructions and complex reasoning, making prompting a flexible interface for adapting models without parameter updates. Yet prompt design remains labor-intensive and highly sensitive to formatting, phrasing, and instruction order, motivating automated prompt optimization methods that reduce manual effort while preserving inference-time flexibility. However, existing methods often search over prompt candidates or use fixed critique-refine pipelines driven by individual examples or small batches, limiting their ability to capture systematic error patterns and make targeted edits grounded in failure history. We propose Reflective Prompt Tuning (RPT), a framework that uses LLM function calling to simulate the iterative workflow of human prompt engineers. An LLM optimizer calls a diagnostic function that evaluates the target model over an entire optimization set, summarizes recurring failure modes, and returns a structured diagnostic report. The optimizer uses this report, together with an accumulated memory of prior reports, to revise the prompt for the next iteration. RPT further supports confidence-aware optimization by using calibration signals in diagnostic feedback and final prompt selection. Across three reasoning tasks, RPT improves over initial prompts by up to 12.9 points, remains competitive with state of the art, and improves confidence calibration. Our analyses show that RPT is especially effective on multi-hop and mathematical reasoning, producing targeted prompt revisions that align with diagnosed failure patterns and lead to gains in task performance and calibration.
