Ch 7 Introduction to Open-Domain QADesign

The end-to-end blueprint for answering questions over a large knowledge base.

Core concepts

  • Open-domain QA (ODQA). Answer questions where the answer lives somewhere in a large corpus, not a single provided passage.
  • Retriever → Reader. A retriever finds the most relevant passages (vector search); a reader extracts or generates the answer from them.
  • Three components. Vector database (stores context embeddings), retriever (embeds query, fetches top-K), reader (produces the answer).
  • The direct ancestor of RAG. Swap an extractive reader for a generative LLM and you have Retrieval-Augmented Generation.
QuestionRetrieverVector DBtop-K passagesReader / LLMAnswer
The retriever–reader pipeline. Replace the reader with a generative LLM and you have RAG.

What you must master

  • Draw the full ODQA pipeline and name each component’s job Level 1
  • Explain how ODQA maps onto modern RAG Level 2
  • Reason about where quality is lost (retrieval miss vs. reader error) Level 3
  • Design the pipeline for a given corpus size, latency & accuracy target Level 3

Architect’s lens

This is the reference architecture you will draw on whiteboards constantly. The critical insight for design and debugging: most RAG failures are retrieval failures — if the right passage never reaches the reader/LLM, no amount of prompt engineering saves you. Instrument retrieval quality first.