Building Production RAG Systems with LangChain and Next.js

Retrieval-Augmented Generation (RAG) is the dominant architecture for production AI applications. It solves the hallucination problem by grounding LLM responses in your own data.

The RAG Pipeline

A RAG system has three stages: ingestion (chunk and embed your documents), retrieval (find relevant chunks via vector similarity), and generation (pass context to the LLM with the user query).

LangChain Integration

LangChain provides the orchestration layer that connects your vector store, embedding model, and LLM into a coherent pipeline. The new LangChain Expression Language (LCEL) makes composition clean and type-safe.

Streaming with Next.js

Use the Vercel AI SDK with Next.js route handlers to stream LLM responses directly to your UI. Users see tokens appear in real-time rather than waiting for the full response.