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How to plan a RAG architecture for document Q&A with AI System Design

Use the RAG sections to choose chunking, search, and reranking for a document assistant.

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

Who it is forAn AI engineer who must build an assistant that answers questions over company documents and needs to choose the retrieval design first.

The problemRetrieval quality decides whether a RAG assistant answers correctly, and the design choices for ingestion, chunking, and search are easy to get wrong.

The outcomeA written RAG design that names the parsing, chunking, hybrid search, and reranking choices with reasons.

What you need first

  • A web browser to open the README at https://github.com/amitshekhariitbhu/ai-system-design
  • Basic familiarity with software system design concepts such as caching, load balancing, and queues
  • A practice problem to design, such as a document Q&A assistant or a customer support agent

Steps

  1. Open the RAG (Retrieval Augmented Generation) section of the README at https://github.com/amitshekhariitbhu/ai-system-design.
  2. Read Document Parsing and Ingestion and Chunking Strategies, then write down the chunking approach for your documents.
  3. Read Hybrid Search and Reranking, then decide whether your queries need keyword matching, semantic matching, or both.
  4. Read the Vector Database section and pick an index type for your data size.
  5. Write a one-page design that lists each choice, the reason for it, and how you would measure retrieval quality.

Prompt for your agent

Using the repo amitshekhariitbhu/ai-system-design, read the RAG section and produce a retrieval design for <target>, covering chunking, hybrid search, and reranking. Output the design as a Markdown document with one section per choice. Verify that each choice cites the README section it came from.

Caveat. The README explains the RAG options but does not provide code, so you still need to implement and test the chosen design yourself.

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