Question 1

What is a Large Language Model and how does it generate text?

Accepted Answer

**Target Audience:** Beginners to AI engineering — you can write code and call an API, but you have never looked inside a language model.
**Difficulty:** Easy
**Verified with:** Node 22.22.0 and Python 3.12.3 (the toy model in the example; no real model is called). Product and paper facts are cited inline with the date they were 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 w

Question 2

What are hallucinations and what causes them?

Accepted Answer

**Target Audience:** Anyone new to building with LLMs who has seen a model state something false with total confidence and wants to understand why, in plain terms.
**Difficulty:** Easy
**Verified with:** Node 22.22.0 and Python 3.12.3 (a counting toy, an expected-score calculation and a string-matching grounding check; no model is called). Paper and case facts are cited inline with the date they were checked (October 2026).

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

Question 3

AI observability: how do you trace and log LLM calls in prod?

Accepted Answer

**Target Audience:** Developers who have an LLM feature or agent in production and want to see each model call, tool call and its timing; no prior observability experience assumed.
**Difficulty:** Easy
**Verified with:** Node 22.22.0 and Python 3.12.3 (the in-process tracer below, identical output in both). OpenTelemetry convention names, the W3C header format and SDK options were checked with web searches in October 2026; where only secondary sources were found, the text says so.

> **How to re

Question 4

How do you get reliable structured JSON output from an LLM?

Accepted Answer

**Target Audience:** Engineers whose LLM feature has to hand JSON to ordinary code (a parser, a database, an API) and who want it to stop breaking.
**Difficulty:** Medium
**Verified with:** Node 22.22.0 and Python 3.12.3 (the constrained-decoding simulation below). Provider API shapes were checked against the vendor docs and announcements cited inline, in October 2026.

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

Question 5

How do you build a golden dataset for evaluating an LLM feature?

Accepted Answer

**Target Audience:** Engineers who know why evals matter and now have to assemble the dataset behind them, and candidates asked to design one in an interview.
**Difficulty:** Medium
**Verified with:** Node 22 and Python 3.12 (a dataset builder over 200 simulated traces, and an inter-annotator agreement calculation). The traces and labels are synthetic and stated as such; no model is called.

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

Question 6

What is the Model Context Protocol (MCP) and what problem does it solve?

Accepted Answer

**Target Audience:** Engineers who have used tool calling and keep hearing "just ship an MCP server", and anyone preparing for an AI engineering interview.
**Difficulty:** Medium
**Verified with:** Node 22.22.0 and Python 3.12.3 (both examples, identical output). Protocol details were checked in October 2026 against the published SDK packages themselves: `@modelcontextprotocol/sdk` 1.32.1 (the v1 line, spec up to 2025-11-25), `@modelcontextprotocol/server` and `@modelcontextprotocol/core` 2.3.1

Question 7

What are reasoning models and when should you use them?

Accepted Answer

**Target Audience:** Engineers who know how a regular LLM API call works and need to decide where a "thinking" model belongs in a product.
**Difficulty:** Medium
**Verified with:** Node 22.22.0 and Python 3.12.3 (the example is deterministic arithmetic and stub answers; no real model is called). 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

Question 8

How do temperature, top-p and other sampling parameters affect output?

Accepted Answer

**Target Audience:** Developers who call LLM APIs and want to set sampling parameters on purpose rather than by copying defaults.
**Difficulty:** Medium
**Verified with:** Node 22.22.0 and Python 3.12.3 (the sampling pipeline in the example runs on a fixed logits vector; no real model is called). API parameter rules 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 ter

Question 9

How do you reduce hallucinations in a production LLM application?

Accepted Answer

**Target Audience:** Engineers who own an LLM feature that real users rely on (support bots, internal assistants, document Q&A) and need to make it stop inventing things, measurably.
**Difficulty:** Medium
**Verified with:** Node 22.22.0 and Python 3.12.3 (the four-layer scoring run below). Case law, papers and provider features were checked via the sources cited inline, in October 2026.

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

Question 10

What are RLHF and DPO, and how do they turn a base model into an assistant?

Accepted Answer

**Target Audience:** Engineers who use chat models every day and want to know what happened between "a model that continues text" and "an assistant that answers questions", without a reinforcement-learning background.
**Difficulty:** Medium
**Verified with:** Node 22.22.0 and Python 3.12.3 (a four-response toy, solved exactly; no real model is trained or called). Paper facts are cited inline with the date they were checked (October 2026).

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

AI ENGINEERINGDECODED.

Easy

What is a Large Language Model and how does it generate text?

What are hallucinations and what causes them?

AI observability: how do you trace and log LLM calls in prod?

Medium

How do you get reliable structured JSON output from an LLM?

How do you build a golden dataset for evaluating an LLM feature?

What is the Model Context Protocol (MCP) and what problem does it solve?

What are reasoning models and when should you use them?

How do temperature, top-p and other sampling parameters affect output?

How do you reduce hallucinations in a production LLM application?

What are RLHF and DPO, and how do they turn a base model into an assistant?

Hard

How do you version, test and safely roll out prompt changes?

How do you monitor LLM quality drift in production?

What is red-teaming an LLM application and how is it done?