Retrieval for RAG: chunking, hybrid search, reranking
- RAG
- Asked in most interview loops
- About 2 h to learn
- 4 vetted resources
What this topic covers
Retrieval-augmented generation answers questions from documents the model wasn't trained on. Quality is decided before the LLM sees anything: how documents are chunked, whether keyword (BM25) and embedding search are combined, and whether a reranker reorders the results. Interviewers want you to reason about failure cases and how you'd measure retrieval quality.
Leads to
- GenAI system design
- Evaluating RAG: retrieval metrics and faithfulness
- Advanced RAG: query rewriting, reranking, agentic RAG, long context vs RAG
- Context engineering and agent memory
- LLM and agent security
- RAG vs fine-tuning vs prompting: choosing the approach
- Document parsing and ingestion for RAG: PDFs, tables, OCR
- Design an AI customer-support agent
- Design enterprise document search / a knowledge assistant
- Design a coding copilot
- Rerankers and fine-tuning embedding models
- Text-to-SQL and LLMs over structured data
You’re ready when you can
What interviewers check, from the resources below.
- Explain why chunks lose context and how adding context back helps
- Explain why BM25 + embeddings beats either alone
- Place a reranker in the pipeline and state its latency/cost trade-off
- Name how you'd measure retrieval quality (e.g. recall@k)
- Build keyword search → prompt → answer end to end
- Explain what changes when retrieval becomes a tool the model calls
- Explain when hybrid search beats pure vector search
- Name two retrieval failure modes and a mitigation for each
- Explain why RAG is preferred over fine-tuning for adding knowledge
- Sketch a RAG system with its offline (indexing) and online (query) halves
What to study, in order
Each link opened and checked by hand; the date says when.
Start here
Introducing Contextual Retrieval (opens in a new tab)
Anthropic · 20 min · Free · checked 20 Oct 2026
- Why
- A compact, production-tested walkthrough of hybrid search + reranking, with measured gains: great material for a RAG design answer.
- What to read
- Read “A primer on RAG” through “Further boosting performance with Reranking”.
- What to skip
- The appendix tables.
Free alternative
LLM Zoomcamp, Module 1: Agentic RAG (opens in a new tab)
GitHub · DataTalksClub/llm-zoomcamp · 90 min · Free · checked 20 Oct 2026
- Why
- Build a small RAG pipeline yourself; having built one makes design answers far more concrete.
- What to read
- Do 01-agentic-rag; skim 02-vector-search and the hybrid search / reranking part of 06-best-practices.
- What to skip
- 03-orchestration and the full capstone project.
Go deeper
Patterns for Building LLM-based Systems & Products (opens in a new tab)
eugeneyan.com · Eugene Yan · 25 min · Free · checked 20 Oct 2026
- Why
- A widely cited map of LLM system patterns; a good frame for a system design answer.
- What to read
- Read only “Retrieval-Augmented Generation: To add knowledge” (and “Evals” if you're on the evals topic).
- What to skip
- Fine-tuning, caching and defensive UX for now. The full piece is a 66-minute read.
Go deeper
Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks (opens in a new tab)
arXiv · Patrick Lewis et al. · 40 min · Free · checked 22 Oct 2026
- Why
- This paper named RAG, and interviewers like to check that you know the original was a jointly trained retriever and generator, not just “stuff documents into a prompt”.
- What to read
- Read §2 with Figure 1: §2.1 (RAG-Sequence versus RAG-Token), §2.2 (DPR retriever), §2.4 (what is trained and why the document encoder is frozen). Then read §4.5 on hot-swapping the index to update knowledge without retraining.
- What to skip
- Skip §2.5 decoding details, §3–4.4 per-task results beyond the headline tables, §5 related work and the broader impact section.
Companies that test RAG most
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