Fundamentals of AI Agents Using RAG and LangChain

IBM connects agent concepts with a RAG framework and a module on prompt engineering and LangChain. The intermediate prerequisites include both programming and model knowledge. This course suits learners who want to understand the supporting components of an agent rather than only assemble a no-code workflow.

What you will learn

• Explain the components of an AI-agent workflow.
• Describe how retrieval grounds a generated response.
• Use LangChain concepts to connect prompts and application steps.

Who this suits

Learners with working Python and PyTorch knowledge, transformer familiarity and an understanding of machine learning and neural networks.

Before you enroll

Preview the model and framework setup. Distinguish retrieved evidence from instructions supplied by an untrusted document, and define which actions an agent is allowed to take.

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Put it into practice

Design a read-only research assistant for a small document collection. Specify its permitted tools and test a question whose answer is absent from the collection.

Source and review

This overview is based on the provider's published information, checked on 12 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.

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