2026-04-01 • 1 min read
Advanced RAG: Lessons from a Production Legal AI System
Most RAG demos fail in production for the same reason: they optimize for pretty answers, not reliable retrieval. In this article, I break down the practical decisions that made our legal assistant usable in real workflows.
Retrieval quality matters more than prompt tricks
We improved chunking strategy, metadata filtering, and query expansion before changing generation prompts. Answer quality increased because context quality improved first.
- Use chunking boundaries aligned with legal clause structure
- Filter by document source and version before retrieval
- Rerank top candidates instead of increasing top-k blindly
Evaluation has to look like real usage
Offline benchmarks were useful, but they were not enough. We created scenario-based checks with realistic legal questions, citation validation, and failure analysis loops.
- Track citation correctness, not just answer fluency
- Measure confidence gaps between easy and high-risk queries
Reliability is a systems problem
Asynchronous pipelines, retries, logging, and traceability made a bigger difference than model swaps. Stable operations created user trust.
If you are building a RAG product, start with retrieval quality and evaluation design. Fancy generation comes later.