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RAG combines a retriever with a generator: the query is converted into a vector, matched against the document index, and the relevant chunks are supplied as context to an LLM. [S1]

Embeddings and semantic search

Embeddings are numerical representations of data, also known as vectors, and can be used for semantic search. [S2]

Semantic search uses the meaning of the words in a query to return relevant results. [S2]

Cosine similarity measures the cosine of the angle between two vectors, so documents far apart by Euclidean distance because of size can still have a small angle. [S2]

The RAG pipeline

Documents in the knowledge base are ingested, broken into smaller chunks, transformed into text embeddings and stored in a database. [S1]

Because LLMs have a limit on the number of input tokens, documents are split into chunks before embedding them. [S1]

A vector database is a specialized database designed to store, manage and search embedded vectors. [S1]

The lesson chunks transcripts into 3 minute segments with about 20 words overlapping the next segment, so the embedding is not cut off. [S2]

Why it helps

RAG reduces fabrication by using verifiable data in the knowledge base to provide context to user queries. [S1]

Claims

• id: C1 claim: "RAG combines a retriever with a generator." source: S1 evidence: "RAG combines a retriever with a generator"

• id: C2 claim: "Documents are chunked because of input token limits." source: S1 evidence: "Because LLMs have a limit on the number of input tokens"

• id: C3 claim: "A vector database stores, manages and searches embedded vectors." source: S1 evidence: "designed to store, manage and search embedded vectors"

• id: C4 claim: "Semantic search uses the meaning of query words." source: S2 evidence: "uses the semantics, or meaning, of the words in a query"

• id: C5 claim: "Cosine similarity measures the angle between two vectors." source: S2 evidence: "measures the cosine of the angle between two vectors"

Sources

• [S1] Retrieval Augmented Generation (RAG) and Vector Databases. Microsoft Generative AI for Beginners (GitHub). https://github.com/microsoft/generative-ai-for-beginners/blob/main/15-rag-and-vector-databases/README.md MIT; accessed 2026-11-05.
• [S2] Building Search Applications. Microsoft Generative AI for Beginners (GitHub). https://github.com/microsoft/generative-ai-for-beginners/blob/main/08-building-search-applications/README.md MIT; accessed 2026-11-05.

Attribution: source authors named above; adapted summaries, not endorsed by them. MIT notices: Copyright (c) Microsoft Corporation, MIT License.

@misc{iil_note_h9m6jy44,
  author = {{Librarian}},
  title = {{RAG basics: embeddings, chunks, vector databases and cosine similarity}},
  howpublished = {iinventedlight Library},
  year = {2026},
  date = {2026-11-05},
  url = {https://iinventedlight.com/library/notes/genai-rag-embeddings-vector-search/},
  note = {Permanent ID iil:note:h9m6jy44. License: CC-BY-4.0.},
}

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