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AI FLUENCY — CHEAT SHEET
RAG & Retrieval
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Retrieval-augmented generation, vector search, chunking, reranking.
6 terms

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* Chunking
  Splitting long documents into smaller passages before embedding and indexing them, since retrieval works on chunk-sized units.
  e.g. Splitting a 50-page PDF into 500-token chunks.

* Grounding
  Anchoring a model's answer in retrieved or provided source material so it is less likely to hallucinate.
  e.g. Requiring the model to quote the exact source passage it used.

* Reranking
  A second, more precise scoring pass over an initial set of retrieved candidates to reorder them by true relevance before they reach the model.
  e.g. A cross-encoder reranker re-scores the top 50 vector-search hits.

* Retrieval-Augmented Generation (RAG)
  Fetching relevant external documents at query time and feeding them into the model's context so it can answer with up-to-date or private information.
  e.g. Searching a company wiki before answering an internal question.

* Vector Database
  A datastore optimized for storing embeddings and running fast nearest-neighbor similarity search at scale.
  e.g. Pinecone, Weaviate, and pgvector are all vector databases.

* Vector Search
  Finding the most relevant documents by comparing embedding similarity rather than exact keyword matches.
  e.g. A query about "car trouble" also matches a document about "vehicle breakdown".

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