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How to design a RAG pipeline from the retrieval lessons with AI Engineering Course

Use the RAG module to list the retrieval options before you build a pipeline.

  • Research
  • Intermediate
  • 30 minutes
  • From the README
  • Any agent

Who it is forA developer who has been asked to add search over company documents to an LLM app and needs to choose retrieval methods.

The problemRAG has many options such as chunking, hybrid search, rerankers, and query rewriting, and it is unclear which ones to use.

The outcomeA design note that lists the RAG components to consider, with the lesson that covers each one.

What you need first

  • A web browser to open the GitHub repo and read the lessons
  • Basic programming knowledge, preferably Python, for the code examples
  • Time to read lessons in order, module by module

Steps

  1. Open Module 9: Vector Search and Retrieval-Augmented Generation (RAG) in the README.
  2. List the topics: vector databases, ANN search, semantic search, hybrid search, rerankers, and chunking.
  3. List the advanced topics: Contextual Retrieval, HyDE, query rewriting, multi-query retrieval, and GraphRAG.
  4. Mark which topics fit your document set and your latency budget.
  5. Write the design note with one line for each chosen topic and the lesson to read before you build it.

Prompt for your agent

Read Module 9 in the README of amitshekhariitbhu/ai-engineering-course and draft a RAG design note for <target document set>. Output a list of chosen retrieval components with the matching lesson names, and verify that each component appears in the Module 9 topic list.

Caveat. The README lists RAG topics but does not include code or a build guide, so you still need to write and test the pipeline with other tools.

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