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

What is a vector database and when do you actually need one?

Accepted Answer

**Target Audience:** Engineers starting a RAG or semantic search project who keep being told to "add a vector database" and want to know what that is and whether they need one yet.
**Difficulty:** Easy
**Verified with:** Node 22.22.0 and Python 3.12.3 (the sizing, filtered search and sync simulation below). The per-core speed in the sizing table is a stated assumption, not a measurement. Product facts are cited inline with the date checked (October 2026).

> **How to read this doc:** Every idea

Question 3

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 4

How do multimodal models handle images, audio and other non-text inputs?

Accepted Answer

**Target Audience:** Engineers who have used text-only LLM APIs and now need to send screenshots, scans, photos or audio.
**Difficulty:** Medium
**Verified with:** Node 22.22.0 and Python 3.12.3 (the patch-projection demo and the token estimators below; no real model is called). Provider token rules and paper details are cited inline with the source and the date checked (October 2026).

> **How to read this doc:** Every idea is explained in plain language first, then tagged with `📌 Interview te

Question 5

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 6

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 7

pgvector vs Pinecone vs Qdrant: how do you choose a vector DB in 2025?

Accepted Answer

**Target Audience:** Engineers and tech leads who have decided they need vector search and must pick between an extension in Postgres, a managed service and an open-source engine, then defend the choice.
**Difficulty:** Hard
**Verified with:** Node 22.22.0 and Python 3.12.3 (the filtered-search simulation below). Product behaviour, limits and pricing models are cited inline with the source and the date checked (October 2026). The title says 2025; the facts here are as found in October 2026, and

AI ENGINEERINGDECODED.

Easy