Project: Generative AI Applications with RAG and LangChain

IBM uses a document-question-answering project to connect LangChain loaders with retrieval and generation. Three modules develop the loading, RAG and bot-building stages. The provider recommends the earlier Generative AI Engineering with LLMs courses, so this is a consolidation project rather than a first Python lesson.

What you will learn

• Load documents into a LangChain workflow.
• Connect retrieval with a language-model answer.
• Build a bot that answers questions about supplied documents.

Who this suits

Intermediate learners with preparation equivalent to IBM’s preceding Generative AI Engineering with LLMs courses.

Before you enroll

Check the project environment, model access and package versions. Evaluate whether retrieved passages support the answer and include questions that the documents cannot resolve.

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Put it into practice

Build a small document bot and test supported, ambiguous and unsupported questions. Keep the retrieved passages with each answer so failures can be traced.

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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