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How To ACE AI Product Design Interviews (Anthropic PM Mock Interview)

The first AI product design mock interview on YouTube.

Aakash Guptahost

Jan 20, 202639mWatch on YouTube

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Chapter summaries

Why AI product design interviews are the new PM gatekeeper
Aakash sets the stakes: AI product design interviews have become the hardest new PM interview type, especially at top AI labs and AI-feature-heavy product companies. He frames why traditional product design interview prep doesn’t transfer cleanly to AI-first prompts and why this mock interview is different.
- •AI product design questions are now common across top-tier and mainstream companies
- •Examples of AI design prompts (new ChatGPT version, space AI, improving negative feedback)
- •Traditional product design muscle memory isn’t sufficient for AI-specific constraints
- •Promise: a full AI product design mock interview with real evaluation detail
Five AI product design question archetypes + the ‘OpenAI classic’ prompt
The host outlines five recurring categories of AI product design questions and calls out that candidates most often fail on new product design. He introduces the specific prompt used for the mock: designing an AI product to communicate with pets.
- •Five types: product improvement, new product design, platform/API, constraint-based, UX/UI for AI
- •New product design is the most common failure mode
- •OpenAI repeatedly uses a consistent question style over years
- •Setup for the mock interview scenario
Clarifying the prompt: pet type, product integration, and success metric = AGI progress
In the mock, Aakash starts by asking targeted clarification questions to narrow scope and reveal hidden constraints. The interviewer emphasizes that the only success metric is advancing OpenAI’s mission toward AGI, shaping how prioritization must work.
- •Clarify pet scope (single vs many) and the assumption of a large, untapped market
- •Clarify whether the product is standalone or integrated into an existing offering
- •Clarify success metrics—explicitly anchored to AGI mission (not revenue/engagement)
- •Demonstrates interview tactic: create constraints early to guide tradeoffs
A practical interview structure to avoid rambling
Aakash pauses to lay out a clear, end-to-end framework for the answer: users → problems → solutions → prioritization → product design → AGI linkage → risks. This becomes the “map” both candidate and interviewer can follow, reducing drift and repetition.
- •Proposed flow: choose user segment, deeply define problems, brainstorm solutions
- •Then prioritize, design core flows, and explicitly connect to AGI mission
- •Close with safety and risk mitigation considerations
- •Signals structured thinking before diving into ideation
User and buyer segmentation: identifying who pays vs who benefits
He generates user archetypes (new owners, behavior issues, aging pets, trainers, multi-pet households, considerers) and separates the concept of ‘buyers’ from ‘pet types.’ The segment choice is driven by willingness to pay, measurable outcomes, and emotional motivation.
- •Enumerates multiple owner segments and notes distinct needs
- •Separates buyer segmentation from pet-type segmentation
- •Chooses initial focus on dogs/cats due to market size and feasibility
- •Selects ‘pet owners with behavioral issues’ as the primary buyer target
Problem discovery: interpreting pet behavior and capturing context in the moment
Aakash brainstorms the major pain points for owners dealing with problematic behavior, emphasizing the lack of an interpretation layer and the difficulty of observing incidents as they happen. He then groups problems into foundational vs higher-level needs.
- •Core pain: owners don’t know why a pet is doing a behavior
- •Challenges: expensive professional help, contradictory advice, missing the moment of behavior
- •Additional concerns: diet-behavior links and pet-owner fit
- •Prioritizes foundational needs: ‘why’ + ‘catch it when it happens’
Linking the pet product to AGI: empathy, generalization, and scalable understanding
The interviewer pushes for an explicit AGI connection, and together they frame animal understanding as a stepping stone for modeling non-human agents and building empathetic, generalizable intelligence. This AGI lens is then used to justify problem prioritization.
- •AGI linkage: learning to interpret simpler organisms at scale as a generalization step
- •Builds empathy for non-human agents; potential transfer to more complex human contexts
- •Focus on data capture + interpretation as prerequisites for higher intelligence layers
- •Uses AGI mission to reinforce prioritizing foundational observation problems
Solution brainstorming: seven concept directions from hardware to multimodal software
Aakash proposes a broad solution set spanning hardware wearables, multimodal vision apps, home hubs, real-time coaching, diet coaching, pet matching, and a conversation simulator. Each is described at the right altitude—clear, not overly detailed—so they can be compared and prioritized.
- •Smart collar with biosensors that narrates pet state to the owner
- •PetGPT Vision app using phone camera + multimodal analysis
- •Home edge hub for continuous sensing and stimulus playback
- •Real-time behavior coach observing pet+owner interactions
- •Dietician coach, pet match advisor, and a ‘pet voice’ conversation simulator
Prioritizing with a rubric: impact, feasibility, differentiation, engagement
He builds a lightweight scoring table to compare options, and the interviewer checks whether AGI should be added as a criterion. Aakash argues AGI progress will follow from high-impact adoption, and the rubric surfaces real-time behavior coaching as the top bet, with conversation simulation as #2.
- •Evaluation criteria: user impact, technical feasibility, differentiation, engagement potential
- •Tradeoffs discussed: hardware complexity vs reach and iteration speed
- •Real-time behavior coach ranks highest overall; conversation simulator second
- •Interview skill: transparent, explicit prioritization logic rather than intuition
Final direction: combine real-time behavior coaching with ‘conversation’ as the magic moment
Aakash chooses the real-time behavior coach but merges it with the conversation simulator to create emotional stickiness and differentiation. He frames a software-first rollout leveraging OpenAI’s multimodal and natural language strengths and hints at diet coaching as a later module within the same system.
- •Selected problems: ‘why is it happening?’ + ‘catch behavior in the moment’
- •Product strategy: software-first to iterate fast and reach many users
- •Conversation mode positioned as the emotional, sticky differentiator
- •Modular expansion: dietician features as a subset/follow-on
Designing the product: onboarding + passive and active modes
He outlines core user flows: onboarding (pet profile, baseline video, issue description), passive monitoring (key times + baseline model), and active coaching sessions (real-time voice guidance and annotated replay). The design emphasizes building a pet-specific baseline before advanced features unlock.
- •Onboarding: species/breed/age/issues + capture ‘normal state’ video
- •Passive mode: AV monitoring during key moments; 7–14 day baseline modeling
- •Outputs: daily summaries and insights to build trust and habit
- •Active mode: real-time voice coaching and post-session replay with annotations
Conversation mode and key design decisions: voice-first, anti-hallucination, avoid anthropomorphism
Aakash details how ‘talk to your pet’ should unlock only after sufficient observation data and must be tightly grounded in evidence to avoid hallucinations. He highlights major design choices: voice-first interaction, careful framing as interpretation (not translation), visible progress, and hardware-light execution.
- •Conversation mode gated by sufficient data collection (e.g., ~2 weeks)
- •Ground responses in observed behavior; minimize hallucinations
- •Design choices: voice-first UX, avoid incorrect anthropomorphism
- •Include progress visualization for behavior improvement; start hardware-light
Modern prototyping in interviews: how he’d prompt Lovable (instead of drawing every screen)
Prompted by the interviewer, Aakash pivots to demonstrating contemporary PM craft: describing how to generate UI prototypes using an AI tool like Lovable. He explains what to include in prompts—states, triggers, deep links, clickable blocks—so the tool produces a useful first-pass layout.
- •Switches from hand sketches to AI-assisted prototyping approach
- •Prompt focuses on screen states: ‘data collection’ vs ‘post-baseline insights’
- •Design for notifications, deep links, and clickable insight modules
- •Key principle: specify states + interactions; iterate after first layout
AGI framing, risks/mitigations, and a closing user story (Sarah & Max)
Aakash explicitly ties the product back to “Automated General Intelligence” via scalable observation and coaching across non-human agents. He closes with safety/privacy risks and mitigations, then delivers a vivid narrative showing measurable improvement in separation anxiety outcomes and an emotionally resonant ‘conversation’ moment.
- •AGI linkage: automated coaching + generalization beyond humans + research breakthroughs
- •Risks: bad advice, anthropomorphism, privacy from always-on cameras, limited pet coverage
- •Mitigations: disclaimers, confidence scores, interpretation framing, reminders, on-device + delete, phased rollout
- •User story: Sarah and rescue dog Max; measurable reduction in incidents + emotional payoff
How the mock was evaluated + cohort pitch and wrap-up
Bart breaks down why the answer scored highly: strong structure, clear narration, creative yet understandable solutions, transparent prioritization, and strong user centricity (focusing on paying humans). The episode ends with details about the Land PM Job cohort and calls to subscribe and check bundled tools.
- •Evaluation dimensions: execution clarity, creativity, prioritization rigor, user centricity
- •Common failure modes: wandering, weak structure, over-focusing on tech/AGI at expense of user
- •Cohort offer: live sessions, mock interviews, biweekly 1:1 coaching
- •Final CTAs: apply, subscribe/follow, leave reviews, and check the product bundle

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