AI Coding Interview Preparation:
What You Actually Need

AI coding interviews emphasize: (1) Python proficiency over language-agnostic algorithms, (2) Working with APIs and JSON data, (3) Text processing and embeddings, (4) Practical problems over puzzle-style questions. You'll see fewer dynamic programming and more data pipeline tasks. The bar for algorithmic complexity is usually lower, but the bar for clean, production-ready code is higher.

Typical questions include: Build a simple RAG pipeline, Implement semantic search with embeddings, Parse and chunk documents for LLM processing, Build a retry mechanism for API calls, Implement caching for LLM responses, Process streaming responses, Evaluate LLM output quality. Most test your ability to write clean Python that works with LLM APIs.

Less than for traditional SWE roles. Most AI interviews stick to medium-difficulty array/string problems. Focus on: hash maps, two pointers, basic recursion, and tree traversal. Skip hard DP problems unless targeting FAANG. Spend more time on Python-specific skills and LLM integration patterns than algorithm puzzles.

With a software engineering background: 2-4 weeks of focused practice. Focus areas: (1) Week 1-2: Python patterns and LLM API integration, (2) Week 2-3: Practice problems combining data and LLMs, (3) Week 3-4: Mock interviews and timed practice. If you're newer to Python, add 2-4 weeks for fundamentals first.

Key factors: (1) Read requirements carefully and meet all criteria, (2) Write clean, documented code with type hints, (3) Include a README explaining your approach and trade-offs, (4) Add basic tests showing your code works, (5) Don't over-engineer—match the scope to the time given. A clean, working solution beats a complex half-finished one.

1-2 hours daily is optimal for interview prep. Split between: 30 minutes on algorithm practice, 30 minutes on Python patterns, 30-60 minutes building mini LLM projects. Consistency beats marathon sessions. Most candidates see significant improvement in 3-4 weeks of daily practice.

It probably isn't, and you should hold the money. Most cohort dropouts are people who couldn't articulate what they were shipping when they signed up. That's why the consult exists, and why I turn down most applications. If we get on the call and you can't tell me what you'll have shipped at the end of week 8, I'll point you to the AI Native Engineer community until you can.

Yes, the cohort works for people shipping their first serious AI system whether the goal is to land a senior role or to launch a product. The shipped system serves both equally well.

You need to be able to code in Python or TypeScript. Complete beginners can follow the classroom they get access to before the cohort sessions to come in well-prepared.

It's a four-figure investment that we discuss during the 30-minute consult, alongside whether the cohort is the right fit for your project.

Yes, most attendees do. The live session is one Tuesday a week and the async work fits around your existing schedule, as long as you can carve out roughly 6 hours a week.