Build RAG Applications: Get Started

IBM introduces retrieval-augmented applications through LangChain and LlamaIndex, with Gradio providing an interface for practical examples. It suits learners with Python and introductory LangChain experience who want to connect document retrieval with an application a user can interact with, while checking whether the retrieved evidence actually supports the generated response.

What you will learn

• Understand the stages of a RAG application.
• Explore LangChain and LlamaIndex implementations.
• Prepare an interactive interface with Gradio.

Who this suits

Python programming and familiarity with LangChain for simple generative-AI applications are recommended.

Before you enroll

Model APIs, vector databases and hosting may cost extra. Verify package versions before reproducing demonstrations. Use fictional or permissioned documents and test access controls. Evaluate retrieval quality separately from answer quality, include questions the documents cannot answer and check citations. A working demonstration does not establish factual accuracy, resistance to malicious documents or production reliability.

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Put it into practice

Use a short fictional document set and record the retrieved passage for each test answer, including a question with no supporting passage.

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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