AI Interview Common Mistakes:
What Gets Candidates Rejected

Top coding mistakes: (1) Jumping into code without clarifying requirements—always ask questions first, (2) Silent coding—you must explain your thinking, even if it feels awkward, (3) Not testing—run through examples before saying you're done, (4) Giving up on hard problems—partial progress with clear thinking beats silence, (5) Over-engineering—sometimes brute force is fine to start, (6) Ignoring edge cases—null inputs, empty arrays, negative numbers, (7) Bad variable names—'x' and 'temp' hurt readability. The pattern: most mistakes are about process and communication, not raw coding ability.

System design red flags: (1) Jumping to components without understanding requirements—start with clarifying questions, (2) Going too deep too fast—establish the high-level architecture first, (3) Ignoring scale requirements—'how many users?' matters for your design, (4) No trade-off discussion—every design decision has pros and cons, (5) Forgetting operational concerns—monitoring, failure modes, cost, (6) Over-engineering for AI roles—not every problem needs distributed systems, (7) Not drawing diagrams—visual communication is part of the evaluation. System design tests structured thinking and communication as much as technical knowledge.

Behavioral red flags: (1) Vague answers—'I worked with the team' vs specific actions you took, (2) No STAR structure—rambling stories lose the interviewer, (3) Taking all the credit—'I' is fine but acknowledge team contributions, (4) Only positive stories—failure stories with learning show self-awareness, (5) Badmouthing previous employers—even if justified, it looks bad, (6) Not preparing enough stories—scrambling to find examples mid-interview, (7) Stories that don't match the question—listen carefully to what's being asked. Practice 10-15 stories and map them to common behavioral questions.

AI interview red flags: (1) Not knowing your own projects deeply—you built it, you should explain every decision, (2) Overstating capabilities—saying your RAG system had 95% accuracy without metrics to back it up, (3) Confusing AI hype with reality—know the limitations of current LLM technology, (4) No production experience—if all your AI work is notebooks, that's a gap, (5) Can't explain trade-offs—why did you choose GPT-4 over Claude? Why this embedding model?, (6) No cost awareness—production AI has real cost implications, (7) Ignoring evaluation—how did you measure if your system actually works?

Universal interview mistakes: (1) Poor time management—arriving late (even 1 minute) starts you negative, (2) No questions for the interviewer—signals low interest, (3) Appearing desperate—desperation repels, confidence attracts, (4) Not researching the company—basic questions about what they do are embarrassing, (5) Negative body language—crossed arms, no eye contact, fidgeting, (6) Over-talking—concise answers beat rambling ones, (7) Under-preparing—'winging it' rarely works for competitive roles, (8) Not following up—a thank-you email is still expected.

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.

You'll spend 3 hours every Tuesday in the live session and roughly 3 hours of async work in between, for 8 weeks. The Tuesday session time is fixed.

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.