Building Production RAG Systems: Lessons from 6.7M+ Legal Records
When I built CourtGPT.ai to serve every US statute and case law, I learned a lot about what works (and what really doesn't) when taking RAG systems from demo to production. Here's what 18 months of running 6.7M+ legal records at scale taught me.
Why RAG is harder than it looks
Most RAG tutorials show you a toy example: embed a few documents, retrieve them, prompt GPT. That works great when you have 10 documents. It falls apart when you have millions of records, multiple data types (statutes, cases, regulations), and need sub-second response times.
The stack that actually works at scale
