Building a Production RAG Pipeline with LangChain and FAISS
What it actually takes to move a retrieval-augmented generation prototype into production — ingestion, chunking, vector search, evaluation and monitoring.
- ai
- llm
- rag
- langchain
Most RAG demos work on the first try and fall apart on the hundredth query. This article walks through the decisions that mattered when I took a LangChain prototype to a live deployment.
Ingestion and chunking
Document quality decides retrieval quality. Clean the text, keep structural metadata like titles and section names, and pick chunk sizes that match how people actually ask questions.
Vector search with FAISS
FAISS is fast and simple to operate. Store the embedding model version alongside the index so you always know which vectors are comparable.
Evaluation before deployment
Build a fixed question set with expected answers and score every change against it. Measuring accuracy, groundedness and refusals catches regressions before users do.
Monitoring in production
Log queries, retrieved chunks and latency. The failures you see in the logs are the best source of new evaluation cases.