Legal Document RAG Pipeline
2025
A retrieval-augmented pipeline for legal-document analysis, built to a strict accuracy bar given downstream legal use.
Impact
- ›Processed hundreds of documents per day in production
- ›Golden-set evaluation methodology with precision/recall tracking on hand-verified test documents
- ›Section-aware chunking and human-review escalation for low-confidence extractions
RAG pipelines are easy to demo and hard to trust in production, especially when the downstream use is legal. This project was built around evaluation rigor as much as retrieval quality.
The problem
Internal teams needed to extract and analyze information from legal documents at volume, but extraction errors in this context aren’t cosmetic — they can lead to real legal exposure. The bar for accuracy was high, and “looks right” wasn’t good enough as a validation method.
What I built
I built the retrieval-augmented pipeline using OpenAI and Anthropic models (chosen per use case), orchestrated with LangChain and LangGraph, backed by a Pinecone vector store with section-aware chunking so retrieved context respected document structure rather than arbitrary text splits.
To validate accuracy, I followed a golden-set evaluation methodology: a hand-verified set of test documents with known-correct extractions, tracked against precision and recall as the pipeline evolved. Low-confidence extractions were routed to human review rather than silently returned.