Course
Retrieval-Augmented Generation with LangChain
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Prerequisites
LLM Application Fundamentals with LangChainLLM Application Evaluation with LangSmithRetrieval-Augmented Generation
Structured Retrieval
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.
Semantic Retrieval
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.
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FAQs
What Python framework does this course use for building RAG systems?
The course uses LangChain, a popular Python framework for building applications powered by large language models, to implement retrieval-augmented generation workflows.
What prior courses should I complete before this one?
You should complete Introduction and Intermediate Python for Developers, plus the LangChain Fundamentals and LangSmith Evaluation courses in the same learning path.
Is this course suitable for beginners in AI?
It is listed as beginner level for AI but assumes intermediate Python developer skills. Prior experience with LangChain fundamentals and LLM application concepts is expected.
How long is this course compared to typical DataCamp courses?
It is a shorter course with 4 chapters and 20 exercises, estimated at about 2 hours. This is roughly half the length of a standard DataCamp course.
What can I build after completing this course?
You will be able to build retrieval-augmented generation pipelines that combine document retrieval with language model generation, enabling AI applications that reference external knowledge sources.
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