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

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

Question 5

How does the transformer architecture and attention work?

Accepted Answer

**Target Audience:** Engineers who know what an LLM does from the outside and want to understand the inside well enough to explain it on a whiteboard.
**Difficulty:** Medium
**Verified with:** Node 22.22.0 and Python 3.12.3 (attention computed by hand on 4 tokens; no real model). Paper facts are cited inline, checked October 2026.

> **How to read this doc:** Every idea is explained in plain language first, then tagged with `📌 Interview term:` so you also learn the exact words an interviewer ex

Question 6

What is semantic caching and when is it safe to use?

Accepted Answer

**Target Audience:** Engineers with repetitive LLM traffic (support bots, FAQ assistants, internal help desks) who have been told a semantic cache will halve the bill, and want to know when that is true and when it serves wrong answers.
**Difficulty:** Hard
**Verified with:** Node 22.22.0 and Python 3.12.3 (a semantic cache with safety rails, and a threshold sweep on labelled pairs). The embedding in both examples is a **toy stand-in** (words mapped to concepts) so the similarity numbers are exp

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