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Retrieval-augmented generation (RAG) combines search with generative AI to produce responses grounded in relevant information. In this module, you learned how an indexing pipeline prepares source content by cleaning it, dividing it into chunks, creating embeddings, and storing it in a searchable index. You then explored how a RAG application retrieves relevant chunks, adds them to a prompt, and uses a language model to generate a response with supporting sources.

You also learned that RAG quality depends on the complete pipeline. Effective solutions evaluate retrieval and generation separately, protect access to source content, keep indexes current, and balance answer quality with latency and cost.

For more information about the concepts discussed in this module, explore the following resources:

• What are large language models (LLMs)?
• What is retrieval-augmented generation (RAG)?
• What is LLM evaluation?