Building RAG Pipelines with LangChain

LLMs don't know your private data. Fine-tuning is expensive and slow to update. RAG allows us to inject relevant context into the prompt at runtime. It's the difference between a generic answer and a business-specific insight.

I use Pinecone for vector storage and OpenAI's `text-embedding-3-small` for embeddings. The trick isn't the retrieval; it's the chunking strategy. RecursiveCharacterTextSplitter with meaningful overlap ensures context isn't severed mid-sentence.

Vector similarity isn't always semantic relevance. I add a Cross-Encoder reranker step (using Cohere) to sort the retrieved chunks before feeding them to the LLM. This dramatically reduces hallucinations.