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Building Your First RAG Application with LangChain

A practical introduction to Retrieval-Augmented Generation and how to build a "chat with your docs" app.

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Building Your First RAG Application with LangChain
Priya Sharma

Priya Sharma

2 Mar 202611 min read

RAG pipelines let you ground LLM responses in your own data by retrieving relevant context before generation. This article walks through the core components — document loaders, chunking, embeddings, vector stores, and retrieval chains — with a simple LangChain example to build a document Q&A application.

#AI#LangChain#RAG

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