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

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 3

How do you catch regressions when changing a prompt or upgrading a model?

Accepted Answer

**Target Audience:** Engineers who own an LLM feature in production and must decide whether a prompt edit or a model upgrade is safe to ship.
**Difficulty:** Medium
**Verified with:** Node 22.22.0 and Python 3.12.3 (the regression gate below, with a seeded simulator). The promptfoo CI integration and the "Adding Error Bars to Evals" paper were checked via web search in October 2026.

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

AI ENGINEERINGDECODED.

Easy

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

Medium

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

How do you catch regressions when changing a prompt or upgrading a model?