Question 1

What are embeddings and what are they used for?

Accepted Answer

**Target Audience:** Engineers who keep hearing "it runs on embeddings" (search, RAG, recommendations) and want to know what an embedding actually is before building with one.
**Difficulty:** Easy
**Verified with:** Node 22.22.0 and Python 3.12.3 (the toy embedding program below; its 6-number word vectors are hand-written stand-ins for a learned model). Product facts are cited inline with the date they were checked (October 2026).

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

Question 2

What is a vector database and when do you actually need one?

Accepted Answer

**Target Audience:** Engineers starting a RAG or semantic search project who keep being told to "add a vector database" and want to know what that is and whether they need one yet.
**Difficulty:** Easy
**Verified with:** Node 22.22.0 and Python 3.12.3 (the sizing, filtered search and sync simulation below). The per-core speed in the sizing table is a stated assumption, not a measurement. Product facts are cited inline with the date checked (October 2026).

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

Question 3

What are evals and why does every LLM feature need them?

Accepted Answer

**Target Audience:** Anyone building on top of an LLM API who has never written an eval, and candidates who need to explain why "it looked good in the playground" is not a test.
**Difficulty:** Easy
**Verified with:** Node 22 and Python 3.12 (an eval harness over two stubbed prompt versions, and a sampling simulation with Wilson intervals). No model is called; the stub replies stand in for a real model. Vendor guidance is quoted with its source, checked October 2026.

> **How to read this doc:**

Question 4

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 5

What is the difference between zero-shot, one-shot and few-shot prompting?

Accepted Answer

**Target Audience:** Beginners writing their first production prompts, and anyone who needs to explain in-context learning precisely in an interview.
**Difficulty:** Easy
**Verified with:** Node 22.22.0 and Python 3.12.3 (a ticket-classifier harness with a deterministic stub model; no real model is called). Paper results are cited inline, 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

Question 6

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 7

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 8

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 9

Embeddings in 2025-26: how do you choose model, dimensions, and distance?

Accepted Answer

**Target Audience:** Engineers setting up retrieval who must commit to an embedding model, a vector size and a metric, and want a defensible way to choose rather than copying a blog post.
**Difficulty:** Medium
**Verified with:** Node 22.22.0 and Python 3.12.3 (the truncation and quantization program below, on seeded synthetic vectors). Model facts, prices and limits are cited inline with the source and the date checked (October 2026); they change often, so treat them as examples of the mechanis

Question 10

When do you use code-based graders versus model-based graders?

Accepted Answer

**Target Audience:** Engineers writing their first eval suite who need to decide, check by check, whether to write a function or ask a model, and candidates who should be able to defend that choice.
**Difficulty:** Medium
**Verified with:** Node 22 and Python 3.12 (five code graders and a stub judge over ten outputs, and a judge-versus-human validation with corrected pass rates). Judge verdicts are canned stand-ins for model calls; token prices in the cost line are assumptions. Vendor tooling is

AI ENGINEERINGDECODED.

Easy

What are embeddings and what are they used for?

What is a vector database and when do you actually need one?

What are evals and why does every LLM feature need them?

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

What is the difference between zero-shot, one-shot and few-shot prompting?

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?

Embeddings in 2025-26: how do you choose model, dimensions, and distance?

When do you use code-based graders versus model-based graders?

What are reasoning models and when should you use them?

How do multimodal models handle images, audio and other non-text inputs?

What is the difference between fine-tuning, LoRA and prompt engineering?

Hard

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?

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

What are common RAG failure modes and how do you debug them?

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