Question 1

What are embeddings and what are they used for?

Accepted Answer

**Target Audience:** Engineers who keep hearing "it runs on embeddings" (search, RAG, recommendations) and want to know what an embedding actually is before building with one.
**Difficulty:** Easy
**Verified with:** Node 22.22.0 and Python 3.12.3 (the toy embedding program below; its 6-number word vectors are hand-written stand-ins for a learned model). Product facts are cited inline with the date they were checked (October 2026).

> **How to read this doc:** Every idea is explained in plain lan

Question 2

Embeddings in 2025-26: how do you choose model, dimensions, and distance?

Accepted Answer

**Target Audience:** Engineers setting up retrieval who must commit to an embedding model, a vector size and a metric, and want a defensible way to choose rather than copying a blog post.
**Difficulty:** Medium
**Verified with:** Node 22.22.0 and Python 3.12.3 (the truncation and quantization program below, on seeded synthetic vectors). Model facts, prices and limits are cited inline with the source and the date checked (October 2026); they change often, so treat them as examples of the mechanis

Question 3

How does similarity search work — cosine, dot product and Euclidean?

Accepted Answer

**Target Audience:** Engineers who have embeddings and now need to find the closest ones, and who want to know what the metric setting on their vector index actually does.
**Difficulty:** Medium
**Verified with:** Node 22.22.0 and Python 3.12.3 (both examples below: the three metrics worked by hand, and a small IVF index measured against exact search on seeded synthetic data). Database defaults and operator names are cited inline with the date checked (October 2026).

> **How to read this doc:**

Question 4

Hybrid search: why combine BM25 with vectors and how does RRF work?

Accepted Answer

**Target Audience:** Engineers implementing or tuning hybrid retrieval who need to explain, and compute by hand, how two ranked lists become one.
**Difficulty:** Medium
**Verified with:** Node 22.22.0 and Python 3.12.3 (raw-score sum, min-max convex combination and RRF with different k, weights and windows, on two fixed result lists; no search engine is called). Engine parameters and papers were checked in October 2026.

> **How to read this doc:** Every idea is explained in plain language first

Question 5

Evaluating RAG with Ragas: faithfulness, answer relevancy, context precision?

Accepted Answer

**Target Audience:** Engineers who run or are about to run Ragas on a RAG system and want to know what each number means, how it is computed, and what to fix when it moves.
**Difficulty:** Medium
**Verified with:** Node 22.22.0 and Python 3.12.3 (a hand-computed version of the four formulas below). Metric definitions were checked against the Ragas documentation pages for v0.2 to v0.4 via web search in October 2026; API details that only appeared in third-party guides are marked as such.

> **How

Question 6

What is hybrid search and why combine BM25 with vector search?

Accepted Answer

**Target Audience:** Engineers whose embedding-only RAG search misses exact terms, and anyone who needs to explain lexical vs semantic retrieval and why teams combine them.
**Difficulty:** Medium
**Verified with:** Node 22.22.0 and Python 3.12.3 (BM25 written from scratch, a deterministic stub embedding, and RRF fusion on an eight-article knowledge base; no model or search engine is called). Engine facts were checked in each vendor's documentation in October 2026.

> **How to read this doc:** Ev

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