Question 1

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 2

How do you build resilient LLM calls with rate limits, retries and fallbacks?

Accepted Answer

**Target Audience:** Engineers putting an LLM feature in front of real users who have seen their first 429 or 529 in production and want a resilience layer that is more than "wrap it in a try/catch and try again".
**Difficulty:** Medium
**Verified with:** Node 22.22.0 and Python 3.12.3 (both examples below run on a fake clock with stub providers, so the timing logic is real but no API is called). Provider behaviour (status codes, headers, SDK defaults, rate-limit algorithms) is quoted from the A

Question 3

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 4

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 5

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 6

How do you evaluate and debug agent trajectories?

Accepted Answer

**Target Audience:** Engineers building tool-using agents who can already evaluate a single LLM response and now need to evaluate, and debug, multi-step runs with real side effects.
**Difficulty:** Hard
**Verified with:** Node 22.22.0 and Python 3.12.3 (the trajectory scorer below). The τ-bench definitions and results, and the LangChain agentevals match modes, were checked via web search in October 2026; the agentevals per-mode rules were only found in secondary sources and are marked as such.

Question 7

How do you validate and repair structured LLM output at runtime?

Accepted Answer

**Target Audience:** Engineers putting LLM output into systems of record (databases, billing, tickets) who need a runtime layer that catches bad output and fixes what can be fixed.
**Difficulty:** Hard
**Verified with:** Node 22.22.0 and Python 3.12.3 (two programs: the validate-repair-fallback pipeline and the truncation demo). Library and provider behaviour is cited inline with the source, checked October 2026.

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

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