Retrieval Augmented Generation (RAG) with LangChain

Across about three hours you build from foundational RAG up to more advanced methods using LangChain, which makes it highly relevant if you're a Python developer who knows LLM basics and wants applications that cite real, current information. Skip it if you don't code, or if you want a concepts-only treatment. On top of the DataCamp subscription (about $14/month billed annually, first chapter free), note the model and embedding APIs you'll use are billed by their providers separately. LangChain evolves fast, so verify the methods against whatever version is current when you take it (as of 2026).

Retrieval Augmented Generation (RAG) with LangChain teaches the technique that lets LLMs answer from your own data instead of just their training set. Across about three hours you progress from foundational RAG through advanced methods — semantic splitting and graph-based retrieval with Neo4j — integrating external knowledge into model responses.