FAPO: Fully Automated Prompt Optimization of Multi-Step LLM Pipelines
Multi-step LLM pipelines fail through interactions among retrieval, reasoning, and formatting steps, so prompt-only optimization can miss bottlenecks in the chain. We present Fully Automated Prompt Optimization (FAPO), a framework that lets Claude Code optimize an LLM pipeline inside a standardized codebase. FAPO evaluates a pipeline, inspects intermediate steps, diagnoses failures, proposes scoped changes, and validates variants repeatedly to optimize against a score function. It first tries prompt edits and, only when prompt optimization appears insufficient, changes chain structure within the permitted scope when attribution identifies a structural bottleneck. Across six benchmarks and...
| Authors | Paul Kassianik, Baturay Saglam, Huaibo Zhao et al. |
|---|---|
| Primary category | cs.SE |
| Published | 17 June 2026 |
| Last updated at source | 20 June 2026 |
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