Question 1

What is RAG and why use it instead of fine-tuning for knowledge?

Accepted Answer

**Target Audience:** Beginners meeting retrieval-augmented generation for the first time, and engineers who need to defend "RAG, not fine-tuning" for a knowledge feature in an interview.
**Difficulty:** Easy
**Verified with:** Node 22.22.0 and Python 3.12.3 (a keyword retriever plus deterministic stub models; no real model or vector database is called). Papers cited inline were checked in October 2026.

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

Question 2

What is an AI agent and how does the agent loop work?

Accepted Answer

**Target Audience:** Engineers who have called an LLM API and now need to build, or explain, something that takes several steps on its own.
**Difficulty:** Medium
**Verified with:** Node 22.22.0 and Python 3.12.3 (the example below, identical output). The tool-calling message shapes were checked in October 2026 against the Anthropic docs page "Handle tool calls" (platform.claude.com) and the OpenAI Agents SDK reference for `Runner.run`.

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

Question 3

How do you choose between open-weight models and proprietary API models?

Accepted Answer

**Target Audience:** Engineers and tech leads who have shipped on a model API and are asked "should we run our own model?"
**Difficulty:** Medium
**Verified with:** Node 22.22.0 and Python 3.12.3 (the break-even model below; every input is a labelled assumption, not a quote). Licence and product facts 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 term:` so you also

Question 4

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 5

When should you split work across multiple agents instead of one?

Accepted Answer

**Target Audience:** Engineers deciding whether an agent feature needs a "multi-agent architecture", and candidates who will be asked to defend that choice either way.
**Difficulty:** Hard
**Verified with:** Node 22.22.0 and Python 3.12.3 (the example below, identical output). Published figures were checked in October 2026: Anthropic's engineering posts "Building effective agents" (December 2024) and "How we built our multi-agent research system" (June 2025), and secondary coverage of Cognition'

Question 6

How would you design a production RAG pipeline end-to-end in 2025-26?

Accepted Answer

**Target Audience:** Engineers preparing for an AI or backend system design round who can explain RAG in one sentence and now need to design the whole thing: ingestion, retrieval, generation, numbers, operations and evaluation.
**Difficulty:** Hard
**Verified with:** Node 22.22.0 and Python 3.12.3 (the miniature pipeline and the sizing arithmetic below). Every input to the sizing is a stated assumption, and the prices in it are illustrative placeholders, not a vendor price list. Product facts ar

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Easy