Question 1

How do you choose the right model for a task?

Accepted Answer

**Target Audience:** Engineers adding their first LLM feature who see a long list of models and want a repeatable way to pick one.
**Difficulty:** Easy
**Verified with:** Node 22.22.0 and Python 3.12.3 (the selection harness below; the models in it are deterministic stubs and the prices are illustrative). 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 `📌 Inter

Question 2

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 3

How do you estimate and control LLM costs in production?

Accepted Answer

**Target Audience:** Engineers asked "what will this cost per month?" before an LLM launch, and anyone whose first invoice was a surprise.
**Difficulty:** Medium
**Verified with:** Node 22.22.0 and Python 3.12.3 (a cost estimator and a metering ledger). All prices in the examples are a **hypothetical price card** with round numbers so the arithmetic is easy to follow; they are not any vendor's current prices. Discount mechanics (cache read and write multipliers, batch discounts) are quoted from

Question 4

Beyond Ragas: how do you eval LLMs with LLM-as-judge, DeepEval, and human eval?

Accepted Answer

**Target Audience:** Engineers who have one eval framework running (often Ragas on a RAG endpoint) and now need to evaluate the rest of an LLM product: summaries, support replies, agents, anything without a neat retrieval score.
**Difficulty:** Medium
**Verified with:** Node 22.22.0 and Python 3.12.3 (the judge-calibration and review-routing example below). DeepEval and G-Eval details were checked against the DeepEval docs, PyPI and the G-Eval paper via web search in October 2026; where only sec

Question 5

How do you design human-in-the-loop approval for agent actions?

Accepted Answer

**Target Audience:** Engineers adding approval steps to agents that can spend money, message people or change data, and candidates asked to design that step.
**Difficulty:** Medium
**Verified with:** Node 22.22.0 and Python 3.12.3 (both examples, identical output). Framework APIs were checked in October 2026: AI SDK `ai` 7.0.143 type definitions (read from the published package), the LangGraph and OpenAI Agents SDK docs (via web search), and the `@modelcontextprotocol/server` 2.3.1 doc comments.

Question 6

Prompt caching in 2025: how do you cut cost with Anthropic & OpenAI caches?

Accepted Answer

**Target Audience:** Engineers who already know what prompt caching is and now have to make it pay on a real bill, on Anthropic, OpenAI or both.
**Difficulty:** Medium
**Verified with:** Node 22.22.0 and Python 3.12.3 (the cost simulator below). Provider behaviour was checked in October 2026 against the Anthropic docs, OpenAI announcements, and the Azure and third-party documentation cited inline where OpenAI's own guide could not be read directly.

> **How to read this doc:** Every idea is expl

Question 7

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

Question 8

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

Accepted Answer

**Target Audience:** Engineers who own an LLM feature in production and need prompt edits to ship as safely as code changes; also anyone preparing for an AI engineering or ML platform interview.
**Difficulty:** Hard
**Verified with:** Node 22.22.0 and Python 3.12.3 (the registry, gate and canary simulation below, identical output in both). Tool behaviour (Langfuse labels, model aliases) was checked with web searches in October 2026 and is cited inline.

> **How to read this doc:** Every idea is

Question 9

How do you monitor LLM quality drift in production?

Accepted Answer

**Target Audience:** Engineers and tech leads running an LLM feature in production who need to know when it is getting worse without anyone having changed it; also anyone preparing for an AI engineering interview.
**Difficulty:** Hard
**Verified with:** Node 22.22.0 and Python 3.12.3 (the 21-day drift monitor simulation below, identical output in both). The published incidents cited were checked with web searches in October 2026.

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

Question 10

What techniques reduce LLM latency at each stage of a request?

Accepted Answer

**Target Audience:** Engineers whose LLM feature works but feels slow, and who want a method for finding and removing latency rather than a list of tricks.
**Difficulty:** Hard
**Verified with:** Node 22.22.0 and Python 3.12.3 (a latency-budget model and a speculative-decoding simulation). The per-stage numbers in the budget are **stated assumptions** used for the arithmetic, not measurements of any provider; vendor claims (prompt caching latency, predicted outputs, speculative decoding speedups

AI ENGINEERINGDECODED.

Easy