Production RAG Implementation: Lessons from Processing 13+ Million Documents
By
tifa2up
Sesame, salt, and substance. A flagship bake.
Summary
The author shares practical lessons learned from building production RAG (Retrieval-Augmented Generation) systems that processed over 13 million documents across two projects: Usul AI (9M pages) and an unnamed legal AI enterprise (4M pages). The article covers the technical journey from initial prototyping with Langchain and Llamaindex to production deployment, highlighting what worked versus wasted time. Key insights include the importance of starting with small datasets for testing, the challenges of scaling to production data, and practical implementation strategies that proved successful in real-world applications.
Key quotes
· 5 pulledWe built RAG for Usul AI (9M pages) and an unnamed legal AI enterprise (4M pages)
We started out with youtube tutorials. First Langchain → Llamaindex
Got to a working prototype in a couple of days and were optimistic with the progress
We run tests on subset of the data (100 documents) and the results looked great
We spent the next few days running the pipeline on the production dataset and got everything working in a week — incredible
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