Question 1

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 2

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 3

Tool calling vs function calling vs structured output — what changed in 2025?

Accepted Answer

**Target Audience:** Engineers who keep meeting three overlapping names in docs, SDKs and interviews and want one clear mental model plus an accurate timeline.
**Difficulty:** Medium
**Verified with:** Node 22.22.0 and Python 3.12.3 (the wire-format adapter below, run against canned API responses). Dates and API shapes are cited inline with their source, checked October 2026. The title says 2025; this answer covers 2025 and notes what has changed since.

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

Question 4

What is prompt caching and how does it reduce cost and latency?

Accepted Answer

**Target Audience:** Engineers who call an LLM API with long, repeated prompts (agents, chat assistants, RAG over a fixed corpus) and want to understand what the "cached tokens" line on their bill means.
**Difficulty:** Medium
**Verified with:** Node 22.22.0 and Python 3.12.3 (the prefix-cache simulation below). Provider behaviour and prices were checked against the Anthropic docs, OpenAI announcements and the other sources cited inline, in October 2026.

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

Question 5

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 6

What is context engineering and how does it differ from prompt engineering?

Accepted Answer

**Target Audience:** Engineers moving from single prompts to RAG systems and agents, who need to explain what changes when "the prompt" becomes a pipeline.
**Difficulty:** Medium
**Verified with:** Node 22.22.0 and Python 3.12.3 (a context assembler run over simulated candidate items with given token counts; no real model is called). Origins of the term and published guidance are cited inline, checked October 2026.

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

Question 7

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 8

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 9

How do you design good tool definitions for an LLM?

Accepted Answer

**Target Audience:** Engineers turning a backend (REST endpoints, database queries, internal scripts) into tools for an LLM or agent, and anyone who has watched a model pick the wrong tool.
**Difficulty:** Medium
**Verified with:** Node 22.22.0 and Python 3.12.3 (a tool-definition linter and a tool-selection eval). Vendor guidance is quoted from the Claude docs ("Define tools", "Handle tool calls"), Anthropic engineering posts and other sources as cited, checked October 2026.

> **How to read th

Question 10

What are guardrails and how do you implement them around a model?

Accepted Answer

**Target Audience:** Engineers adding an LLM feature to a product who need to explain, and build, the checks that run around the model call.
**Difficulty:** Medium
**Verified with:** Node 22.22.0 and Python 3.12.3 (both examples below, identical output). Product facts (Llama Guard 4, Prompt Guard 2, NeMo Guardrails rail types, Guardrails AI on-fail actions, the OpenAI moderation endpoint, Amazon Bedrock ApplyGuardrail) were checked by web search in October 2026 and are cited where they appear.

AI ENGINEERINGDECODED.

Medium

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

How do you estimate and control LLM costs in production?

Tool calling vs function calling vs structured output — what changed in 2025?

What is prompt caching and how does it reduce cost and latency?

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

What is context engineering and how does it differ from prompt engineering?

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

How do you reduce hallucinations in a production LLM application?

Hard

How should an agent handle tool errors, timeouts and retries?

How do you sandbox an agent and enforce least-privilege tool access?

Guardrails for LLM apps: how do you implement input/output safety in 2025?

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

How do you evaluate and debug agent trajectories?

MCP authoring: how do you build a Model Context Protocol server in 2025-26?

What is prompt injection and how do you defend against it?

Guardrails for AI agents: rate limits, budget caps, and HITL?