CourtGPT Developer Shares RAG System Lessons From 6.7M Legal Records

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The developer behind CourtGPT.ai spent 18 months building a production-scale retrieval-augmented generation system covering all US statutes and case law, processing over 6.7 million legal records. The system combines pgvector for vector search, OpenSearch BM25 for keyword matching, and Anthropic Claude Sonnet 4 for legal reasoning, achieving 92% recall at top-10 results through hybrid search. Semantic chunking that respects legal document structure — such as section and citation boundaries — was found to be far more effective than fixed token windows at scale. The platform targets a hallucination rate below 2% and a 95th-percentile response time under two seconds, validated through 500-plus weekly test queries. Per-query costs range from roughly $0.51 to $1.65, which the developer says is sustainable under a $20-per-month subscription model.

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