FAPO: Fully Autonomous Prompt Optimization of Multi-Step LLM Pipelines
Cisco Foundation AI
FAPO evaluates multi-step LLM pipeline outputs, attributes failures to the specific step that caused them, proposes targeted prompt variants, validates them with an independent agent, and iterates until accuracy improves or budget is exhausted. It outperformed GEPA (state-of-the-art optimizer) in 15 of 18 model-benchmark pairs, with mean gains of +14.1 percentage points and +33.8 on tasks requiring structural prompt changes. Open-sourced under Apache 2.0.
Why it matters
Step-level failure attribution is qualitatively different from treating the pipeline as a black box — it enables targeted optimization that pipeline-blind methods cannot achieve.
Importance: 2/5
Novel step-level failure attribution for multi-step LLM pipelines; 30 HF Daily upvotes; open-sourced under Apache 2.0.