Retrieval-Augmented Generation with LangChain

You will learn to build SQL-grounded RAG systems that convert natural language questions into valid SQL queries, validate them safely, execute them against databases, and synthesize results into accurate LLM responses — enabling you to unlock insights from relational data without requiring domain experts to write queries.

You will learn to build semantic RAG systems that retrieve relevant information from unstructured documents using embeddings and vector databases — from preprocessing documents into searchable chunks to implementing real-time semantic search and response generation — enabling you to unlock insights from the majority of enterprise data that exists as documents and free-form text.