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Information Retrieval & Search

Finding the right information in huge collections — the foundation of search engines and of retrieval-augmented generation.

Information Retrieval & Search is one of the core areas in the AI University map of AI. Explore the diagram, then dive into each topic — every subtopic grows into its own deep-dive over time.

flowchart LR
  Q[/Query/] --> LEX[Keyword search]
  Q --> VEC[Vector search]
  LEX --> FUSE[Hybrid fuse] --> RR[Re-rank] --> R[/Results/]
  VEC --> FUSE
  DOCS[(Documents)] -. index .-> LEX
  DOCS -. embed .-> VEC

Key topics

  • Keyword search

    Inverted indexes and ranking functions like BM25 — still strong baselines.

  • Semantic / vector search

    Embedding queries and documents to match on meaning, not just words.

  • Vector databases & ANN

    Approximate nearest-neighbor indexes (HNSW, IVF) that make embedding search scale.

  • Hybrid & re-ranking

    Combining lexical and semantic signals, then re-ranking with cross-encoders.

  • Chunking & indexing

    Turning documents into retrievable units — the unglamorous key to good RAG.

  • Evaluation

    Recall@k, nDCG, and MRR — measuring retrieval quality.

NLP & Large Language Models · Recommender Systems · Building with AI

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