Ultimate RAG Bootcamp Using LangChain, LangGraph & LangSmith

Krish Naik’s focused bootcamp follows retrieval from document ingestion into more advanced search and agent-based approaches. The published curriculum includes hybrid and multimodal retrieval, vector stores, and LangSmith tracing. It is a substantial route for someone whose main problem is connecting model answers with external information.

What you will learn

• Parse and split application documents
• Create and query embedding indexes
• Compare retrieval strategies
• Inspect RAG workflows with tracing tools

Who this suits

Best approached with Python and API familiarity. A first model application will make it easier to understand where retrieval adds value. Allow time to experiment with your own documents rather than only replaying the supplied examples.

Before you enroll

Model calls and hosted search or tracing services can have separate charges. A retrieved passage can still be irrelevant, incomplete, or parsed incorrectly. Evaluate retrieval and answer quality separately, and match framework versions to the course repository.

Paid course access follows Udemy’s current purchase or subscription terms. Check regional pricing, plan inclusion, and completion-certificate conditions on the provider page.

Put it into practice

Build a small knowledge base with answerable and unanswerable questions. Compare two chunking strategies using the same questions and inspect the retrieved evidence before the final answer.

Source and review

This overview is based on the provider's published information, checked on 11 October 2026. It is an editorial assessment, not a claim that we completed the resource. Time filters are approximate study ranges; projects and exercises can take longer.

View the curriculum and current access options at Udemy.

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