SCIPE - Systematic Chain Improvement and Problem Evaluation
llm-evaluationllm-chainsdebugginglangchain
Abstraction: LangChain-featured tool for identifying failing nodes in LLM chains
Key points:
- SCIPE finds the most impactful failing node in an LLM application graph without requiring labeled ground-truth data
- Uses LLM-as-judge to score pass/fail for each node's input/output pairs, then computes independent vs. dependent failure probabilities
- Root-cause algorithm starts from the most downstream node and recursively traverses upstream, stopping when independent failure probability exceeds dependent
- Requires a compiled LangGraph, application response dataframe, and a YAML config (model name, node-to-column mappings)
- Output
EvaluationResultincludesroot_cause,debug_path, and per-node failure statistics convertible to JSON - Supports all LiteLLM-compatible models (e.g., claude-3-5-sonnet-20240620) for the judge step
Connections: Langchain · LLM Evaluation · AI Agents
Source: https://blog.langchain.dev/scipe-systematic-chain-improvement-and-problem-evaluation/