Building a Production RAG Pipeline with Hybrid Retrieval and LangChain

Most RAG tutorials get you 70% of the way there. This is about the other 30% that actually matters in...

mercoledì 1 luglio 2026 New tab

176 words~1 min read

Most RAG tutorials get you 70% of the way there. This is about the other 30% that actually matters in production.

Why basic RAG fails

Embed your docs, retrieve the top-k, pass to the LLM. Simple. But in production you quickly hit a wall. Dense vector search misses exact keyword matches. Keyword search misses semantic meaning. Your retrieval quality plateaus and your LLM starts hallucinating because the wrong context is coming in.

Hybrid Retrieval fixes this

Combine dense vector search with BM25 keyword search, then fuse the ranked results using Reciprocal Rank Fusion. You get the best of both worlds and retrieval precision jumps noticeably.

Building a Production RAG Pipeline with Hybrid Retrieval and LangChain

Building a Production RAG Pipeline with Hybrid Retrieval and LangChain

Related reading

Build a RAG Pipeline From Scratch (Production Patterns That Actually Matter)

Building a Production-Ready RAG Application with LangChain, pgvector, and Gemini

How to build a production RAG pipeline in Python (without a vector database)

Build a RAG application with Runware and LangChain

Production-Grade RAG: Why Vector Search Isn't Enough (and How Hybrid Search…

RAG in production: the failure modes nobody warns you about

Related reading

Build a RAG Pipeline From Scratch (Production Patterns That Actually Matter)

Building a Production-Ready RAG Application with LangChain, pgvector, and Gemini

How to build a production RAG pipeline in Python (without a vector database)

Build a RAG application with Runware and LangChain

Production-Grade RAG: Why Vector Search Isn't Enough (and How Hybrid Search…

RAG in production: the failure modes nobody warns you about