Know what they’re likely to question before they ask.
Paste the job description and whatever your recruiter told you. We’ll show you the rounds worth preparing for, where your background may get challenged, and what we know versus what we’re estimating.
Founder-submitted launch story on AI BusinessSee a complete Full Interview Map exampleBuilt by Anderson Duncan
Not a question bank or a course. Built around one job, one level, and what your recruiter actually told you, for AI, LLM, applied, agent and platform engineering loops.
Senior Applied AI Engineer, Agentic Systems
Halden Systems is fictional. This example separates explicitly stated rounds, inferred preparation priorities, and what remains unknown.
- Evaluation design
- Agent reliability
- Retrieval architecture
- Latency and cost judgment
- Production debugging
Strong agent and retrieval implementation evidence, limited explicit evaluation design story.
Likely interview loop
AI System Design
Named by the recruiter. Agent architecture, retrieval, failure handling, latency, and cost.
Practical Coding
Named by the recruiter. Reading unfamiliar code and changing behaviour, not puzzle speed.
Project Deep Dive
Named by the recruiter. One AI system you shipped, in production detail.
Behavioral
Inferred from senior ownership expectations. A separate round is not confirmed.
Evals & Reliability
Strong role signal, but it may be tested inside system design rather than as a separate round.
Classical ML Theory
No signal establishes a separate theory round. Ask before ruling it out.
Agent Take-home
No usable signal either way. The map keeps the uncertainty visible.
Build one concrete story around an AI feature you shipped: baseline, eval set, metric, the failure you found, the change you made, the measured result.
Stop studying everything.
In 2026 you can be qualified for the job and still have no idea what the interview is. The titles are new and the round names are not standard.
“Next up is your LLM Design round.”
Architecture? Evaluation design? Retrieval trade-offs? Agent failure modes? Each answer is a different week of preparation.
From job description to a ranked loop.
LLMInterview creates job-specific Interview Maps for engineers preparing for AI, ML, and LLM interviews. It uses the job description, optional recruiter notes, and optional candidate background to identify likely rounds, preparation gaps, and priorities. It distinguishes confirmed information from inference and unknowns.
Paste the job description
Start with the text of the role you actually have an interview for.
Add what the recruiter told you
Round names, format, dates. Explicit, unambiguous statements in your notes are treated as confirmed.
Get your Interview Map
The likely loop, what each round tests, where you're exposed, and what to do first.
Every ranking answers why it is ranked there.
- Explicit statements in your recruiter notes are confirmed. We do not independently authenticate those notes.
- Inference is labelled as inference, never dressed up as fact.
- Public reports are supporting evidence, with the publisher shown.
- Unknown stays visible. We never hide a round to look more certain.
- No percentages. Fake precision is still fake.
How this differs from the usual options
A general chat assistant
You can ask a general assistant to do this. LLMInterview builds the evidence labels and preparation priorities into a repeatable workflow, so you don’t have to design that process each time.
Forums and anonymous boards
Useful, scattered, often out of date. Nothing tells you which reports still hold for your level and this loop.
A question bank
Sells volume. Your problem is not volume: you have days, and need to know what to skip.
$39 once. One interview.
No subscription, no credits. The unlock appears on your own map, after the free read.
See a complete Full Interview Map example- What to prepare first
- Round-by-round briefing
- Study / review / skip matrix
- Strength and exposure analysis
- High-priority scenarios with rubrics and follow-ups
- Production-AI and system design drills
- 3-day and 7-day plan
- Methodology and sources
Engineers with a real AI interview already scheduled.
- Applied AI Engineer
- LLM / GenAI Engineer
- Machine Learning Engineer
- Forward Deployed Engineer
- AI Infrastructure Engineer
- Research Engineer
- Data / ML Platform Engineer
- Software Engineer moving into AI
Straight answers.
- Do you know the actual questions?
- No. Nobody legitimately does. We predict the shape of the loop and rank what it tests.
- What if my recruiter told me the rounds?
- Rounds stated explicitly in the notes you provide are marked confirmed. Ambiguous or conflicting notes stay visible so you can clarify them.
- What if you don't know something?
- The map says unknown and keeps it visible. Hiding an unknown round would make the map look better and be worth less.
- Can I paste a link to the job?
- Paste the job description text. We don't read job links. A link can be kept alongside your map as a reference.
- Is this a subscription?
- No. The free map is free. The full map is $39 once, for one interview.
- What happens to what I paste?
- What you paste is stored with your map and sent to our AI provider to generate it. A shared summary may reflect details from those inputs. Review generated content before sharing it, and don’t paste confidential information.
Free guides to the rounds and the roles.
Answer-first: what each round means, what it tests, and what to prepare in the days you have.
Preparing on short notice? Visit the AI Interview Emergency Room for current public prep pressures and a practical three-step response to each.
- AI interview prep: the 2026 guide
The hub. The eight round types AI engineering loops draw from, and how to work out which ones apply to you.
- AI engineer interview: rounds and prep
Coding, AI system design, project deep dive, behavioral, and the signals behind each.
- LLM engineer interview: rounds and prep
Serving, retrieval, agents, evaluation, and the latency and cost judgment this title is screened on.
- AI system design interview: framework and prep
A seven-step framework, and the four ways a model-backed system fails.
- LLM interview questions, organised by what they test
Nine themes instead of a question bank, and why memorised answers fall apart under follow-up.
- RAG interview questions: retrieval, evals and failure modes
Chunking through reranking to context construction, centred on production trade-offs.
- LLM evaluation interview: evals, judges and regressions
Golden sets, task metrics, judge calibration, and online monitoring.
- Agentic AI interview: agent rounds and prep
State, tools, recovery, guardrails, and how agent reliability is actually probed.
Build a map for the interview in front of you.
The likely rounds, what each tests, where you’re exposed, and what to do first.