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 the difference between zero-shot, one-shot and few-shot prompting?

Accepted Answer

**Target Audience:** Beginners writing their first production prompts, and anyone who needs to explain in-context learning precisely in an interview.
**Difficulty:** Easy
**Verified with:** Node 22.22.0 and Python 3.12.3 (a ticket-classifier harness with a deterministic stub model; no real model is called). Paper results 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

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 chunking strategies exist and how does chunk size affect RAG quality?

Accepted Answer

**Target Audience:** Engineers building their first RAG index, and anyone who needs to name and compare chunking strategies precisely in an interview.
**Difficulty:** Medium
**Verified with:** Node 22.22.0 and Python 3.12.3 (six chunkers and a TF-IDF retriever on one handbook; TF-IDF stands in for an embedding model, and no model is called). Library and paper facts were checked in October 2026.

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

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

Question 7

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

Question 8

Why are BLEU and ROUGE poor metrics for LLM outputs, and what replaced them?

Accepted Answer

**Target Audience:** Engineers who have seen BLEU or ROUGE in a dashboard or a paper and need to explain why they mislead for LLM features, and candidates expected to know the modern metric stack.
**Difficulty:** Hard
**Verified with:** Node 22 and Python 3.12 (BLEU, ROUGE-1 and ROUGE-L implemented from scratch, scored on hand-written answers next to a key-fact check, then against different references). Published results are cited by paper, checked October 2026.

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

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