Retrieval-Augmented Generation (RAG) with LangChain
Retrieval-Augmented Generation (RAG) with LangChain
I've been going deep on Retrieval-Augmented Generation (RAG) with LangChain — and it's completely changed how I think about building AI applications. 🧠
Most LLMs are powerful but blind — they don't know your documents, your data, or your context.
RAG fixes that.
What I explored
- 🔍 How to connect raw documents to an LLM through embeddings and vector search
- 🔗 Using LangChain to orchestrate the full pipeline — retrieval, prompt composition, and answer generation
- ⚡ Building a lightweight, practical project that brings all these concepts together
The result
An AI that doesn't just generate — it retrieves, reasons, and responds with context.
If you're curious about building next-gen AI apps that go beyond basic prompting, RAG is the architecture worth understanding.
Happy to share the repo and walk through the approach — drop a comment or DM me. 🚀