LangChain RAG Builder

Build Retrieval-Augmented Generation (RAG) systems using LangChain. Creates document loaders, vector store configurations, retrieval chains, and conversational agents with proper chunking and embedding strategies.

This skill helps you build production-ready RAG applications. It configures document ingestion pipelines, selects optimal chunking strategies, sets up vector stores (Pinecone, Weaviate, Chroma), creates retrieval chains with reranking, and implements conversational memory for chatbot-style interfaces.

When to use

Use when building AI-powered search, knowledge base chatbots, document Q&A systems, or any application that needs to ground LLM responses in your own data.

Examples

Build a docs chatbot

Create a RAG chatbot that answers questions from your documentation

Build a RAG system that ingests our markdown docs and answers developer questions using LangChain and Pinecone

Optimize retrieval quality

Improve answer accuracy with better chunking and reranking

My RAG system gives irrelevant answers 30% of the time — help me improve chunking, add reranking, and tune the retrieval