Grading Prompts
The following are the results of three different prompts I created to grade product sense mock interviews. They are not perfect, but directionally correct.
They are as follows:
General IC Prompt based on common product sense grading rules collected over the years
Leadership Prompt using high-level descriptions
Wendy-Lynn Clone Prompt using my stricter rules
If you want to hear the original mock or get access to the grading prompts, sign up for my Substack newsletter here. You want issue no.63.
The following is feedback for a mock interview recording of a solution for musical instruments I did on July 24, 2025 using 3 different grading prompts. They are imperfect, but generally speaking, it is pretty spot on when it comes to general feedback. It doesn’t do the math well and gives the wrong advice for the number of mocks needed, but I am working on improving that.
Grading for General IC
Overall Recommendation: 4 - Hire
High-Level Grading Concepts:
Broke Down Problem: Strong performance - systematically broke down learning music into instruments, user types, and motivations
Identified People Problems: Good identification of therapeutic users and their specific pain points
Drove Value and Impact: Clear focus on therapeutic benefits and stress reduction during current economic climate
Made Intentional Design Choices: Deliberate choice to focus on guitar learning for therapeutic users
Skills Displayed:
Motivation/Mission: Clearly established why learning music remotely matters now (economic stress, need for at-home entertainment)
Target Audience Identification: Systematically segmented users into three categories (natural talent, therapeutic, curiosity)
Explain Why Building for Chosen Users: Strong rationale for focusing on therapeutic users over curious users (higher engagement) and natural talent users (larger market)
Identified Clear Problem to Solve: Narrowed down to “getting started” as the key pain point
Explained Problem Prioritization: Used frequency and severity matrix to prioritize getting started over finding teachers or advanced learning
Creative Solutions: Proposed AI pre-lesson tutor and AR tools for remote learning
Prioritized Features: Clear ranking methodology for solutions
Concise and Structured Communication: Well-organized presentation with clear transitions
Understanding Probing Questions
Clarifying Questions Asked:
Confirmed assumption about Meta PM role and ecosystem expansion
Clarified instrument vs. singing focus
Set geographic assumptions (US/Canada initially, global expansion)
Confirmed approach methodology before proceeding
Interviewer Engagement:
Interviewer asked for recap of user segmentation approach
Minimal probing suggests candidate was thorough in explanations
No significant challenges or pivots requested
Good vs. Weaker Answers
Strong Performance Areas:
Systematic approach to problem decomposition
Clear user segmentation with rationale
Comprehensive pain point identification (10 specific issues)
Structured prioritization methodology
Connected solutions back to therapeutic goals
Areas for Improvement:
Could have explored more creative AR solutions beyond basic positioning
Limited discussion of competitive landscape
Didn’t fully explore how Meta’s social capabilities could enhance learning
Motivation/Mission
Problem Importance:
Economic uncertainty creating demand for at-home entertainment options
Rising stress levels driving need for therapeutic outlets
Therapists recommending music learning for stress reduction
Universal desire to learn music with limited accessible options
Meta’s Strengths:
Platform excels at connecting people and building community
Focus on community-based learning rather than professional certification
Existing groups already organically forming around music learning
Target Audience
Initial Segmentation:
Adults vs. children (chose adults due to lack of structured learning options)
Three motivation categories identified:
Natural talent seeking to rediscover abilities
Therapeutic outlet for stress reduction
Curiosity-driven hobby exploration
Final Selection Rationale:
Chose therapeutic users over curious users despite smaller market size
Reasoning: Higher engagement potential (unlike “gym membership in January” behavior)
Therapeutic motivation provides sustained commitment vs. casual curiosity
Problem Identification
Comprehensive Pain Point Analysis:
Initial motivation and procrastination barriers
Information overwhelm from search results
Choice paralysis from multiple learning options
Setup and technical configuration challenges
Hand positioning and visual instruction difficulties
Remote instruction limitations vs. in-person guidance
Progress tracking and accomplishment measurement
Bringing skills together into coherent playing ability
Prioritization Framework:
Used frequency and severity matrix for evaluation
Selected “getting started” as highest priority
Rationale: Medium frequency but high severity impact on therapeutic goals
Solutions
Proposed Solutions:
AI Pre-lesson Tutor: Video instruction and illustration for basic lessons to reduce intimidation before meeting with human instructor
AR Tools: Augmented reality positioning assistance for proper instrument setup and hand placement
Integration Strategy: Assumes Meta’s AI search capabilities will solve instructor pairing challenges
Solution Focus:
Emphasis on reducing barriers to initial learning
Bridging gap between complete beginner and productive lesson experience
Leveraging Meta’s technology strengths (AI, AR capabilities)
Communication
Presentation Structure:
Clear methodology explanation upfront
Systematic progression through user types, problems, and solutions
Regular check-ins with interviewer for engagement
Logical flow from broad problem to specific solution focus
Areas of Strength:
Transparent thinking process
Appropriate level of detail for audience
Clear transitions between sections
Effective use of analogies (gym membership, cooking shows)
Feedback for L7+
Focus Areas: Identifying needs
The candidate effectively broke down the large problem space of “learning music remotely” into manageable segments
Clarified scope by distinguishing between singing versus instrument learning
Focused specifically on guitar as the primary instrument due to adult preference patterns
Segmented users by motivation: natural talent, therapeutic outlet, and curiosity
Demonstrated strong user needs analysis by identifying three distinct user motivations
Natural talent group: Adults who had musical ability as children but life circumstances interrupted their development
Therapeutic outlet: People directed by therapists/psychiatrists to learn music for stress relief and creative brain engagement
Curiosity-driven: Adults seeking new hobbies, representing 25-50% of guitar purchases but with low follow-through rates
Selected therapeutic users as primary focus based on engagement potential versus market size analysis
Recognized curiosity group as largest addressable market but lowest retention (compared to gym membership abandonment)
Natural talent group highly engaged but very small market size
Therapeutic group offers balance of engagement and market opportunity
Focus on value and impact
Clearly articulated Meta’s core strength as connecting people and building community rather than professional music instruction
Identified current market timing opportunity
Economic uncertainty from political changes creating demand for low-cost home entertainment
Rising stress levels driving need for therapeutic outlets and alternatives to doom scrolling
Existing organic community formation already happening on Meta platforms
Defined specific value proposition: helping people learn guitar basics for therapeutic stress relief through Meta ecosystem
Positioned solution as community-driven learning rather than competing with certified professional instruction
Recognized stakeholder ecosystem including learners, teachers, instrument companies, and potentially record labels for talent discovery
Making intentional design choices
Developed comprehensive user journey mapping with 10+ specific pain points
Motivation and procrastination challenges
Information overwhelm from multiple sources (Google, ChatGPT, YouTube, Instagram)
Paralysis by choice from too many options
Technical setup difficulties (guitar tuning, hand positioning)
Remote learning challenges with real-time feedback
Achievement and progress measurement for therapeutic users
Prioritized three core problem areas: finding qualified teachers, getting started with basics, and advanced skill development
Selected “getting started learning” as primary focus using frequency/severity analysis
Medium frequency but high severity impact on retention
Most critical for therapeutic user success before habit formation
Proposed three specific solution approaches
AI pre-lesson tutor with video instruction to reduce intimidation before live sessions
AR tools for enhanced remote instruction and positioning guidance
(Third solution mentioned but not fully detailed in transcript)
Handling critique, new data, constraints
Responded appropriately to interviewer requests for clarification
Provided clear recap of user segmentation methodology when asked
Explained decision-making rationale for focusing on therapeutic users
Demonstrated flexibility in assumptions while maintaining structured approach
Made reasonable geographic assumptions (US/Canada initially, global expansion)
Adapted scope based on Meta’s ecosystem strengths
Showed ability to make informed assumptions when given choice
Selected instrument focus over singing based on learning complexity differences
Chose guitar as primary instrument based on adult preference patterns
Interviewer Questions
Key question: Can this person turn big ambiguous problems into great products?
The interviewer provided minimal guidance, allowing the candidate to demonstrate their structured problem-solving approach. Questions were primarily clarifying requests and permission to continue, indicating good pacing and clear communication from the candidate.
Identifying Needs
Rating: 4 - Strong examples
Excellent problem space breakdown and user segmentation
Clear reasoning for target user selection
Comprehensive pain point identification
Focus on value and impact
Rating: 4 - Strong examples
Well-articulated value proposition aligned with Meta’s strengths
Strong market timing rationale
Clear differentiation from professional instruction market
Making intentional design choices
Rating: 3-4 - Acceptable to Strong examples
Systematic approach to solution prioritization
Specific solution concepts with clear rationale
Some solutions not fully fleshed out due to time constraints
Handling critique, new data, constraints
Rating: 3 - Acceptable examples
Responsive to interviewer requests
Demonstrated structured thinking under ambiguous conditions
Limited opportunities to show constraint handling due to interview format
Flags
Time management: The candidate spent significant time on problem definition and user analysis, leaving less time for detailed solution development
Some solutions were mentioned but not fully articulated (third solution concept)
Could have benefited from more concrete metrics or success measurement discussion for the therapeutic use case
More Strict Wendy-Lynn Grading Model
Overall Performance: No Hire (Score: 2)
This was a testing session for transcript analysis rather than a complete mock interview. Based on the extensive notes provided, this appears to be a practice run using a Meta PM prompt about designing a solution for learning music remotely. The candidate demonstrated structured thinking and followed a logical framework, but several critical weaknesses prevent a hire recommendation.
What Went Well
Strong structural approach: Used a clear framework starting with clarifying questions, strategic setup, user segmentation, pain points, and solutions
Effective clarifying questions: Asked relevant questions about Meta PM scope, music vs. instruments distinction, and geographic assumptions before diving into the problem
Good user segmentation: Identified three distinct user motivations (natural talent, therapeutic outlet, curiosity) and provided clear rationale for prioritizing the therapeutic segment
What Went Poorly
Excessive rambling and poor time management: The candidate spoke at length without checking in frequently enough with the interviewer, consuming too much time on setup rather than getting to solutions
Weak strategic justification: The rationale for why this is important “now” focused on Trump, tariffs, and economic uncertainty - a confusing and politically charged explanation that doesn’t clearly connect to Meta’s strategic priorities
Limited solution creativity: Only presented one complete solution (AI pre-lesson tutor) before running out of time, with AR tools mentioned but not fully developed
Clarifying Questions
Effective execution: Asked three solid clarifying questions about Meta PM scope, music definition, and geographic assumptions
Proper check-ins: Confirmed assumptions with interviewer rather than making unilateral decisions
Reduced cognitive load: Questions were focused and helped narrow the problem space appropriately
Strategic Setup
Clear goal articulation: Stated goal as “helping people learn how to play the guitar through the meta-ecosystem”
Weak market timing argument: The Trump/tariffs/economic stress reasoning was poorly constructed and unconvincing
Good Meta positioning: Correctly identified Meta’s strength in community building and connection, though could have been more specific about which Meta products would be leveraged
User Segmentation
Three distinct segments: Natural talent seekers, therapeutic outlet seekers, and curious hobbyists
Sound prioritization logic: Chose therapeutic segment based on engagement potential vs. market size analysis
Good empathy demonstration: Showed understanding of adult learning challenges vs. children’s structured learning environments
Pain Points
Comprehensive user journey: Identified 10+ pain points from motivation through skill development
Therapeutic-specific focus: Pain points were appropriately tailored to the selected user segment
Clear prioritization: Used frequency and severity framework to select “getting started learning” as the priority pain point
Solutions
Incomplete presentation: Only fully presented one solution (AI pre-lesson tutor) due to time constraints
Reasonable first solution: AI pre-lesson concept addresses the prioritized pain point of getting started
Missed opportunity: Failed to present the promised three solutions or demonstrate range of creative thinking
Metrics for Success
Not addressed due to time constraints
Communication
Poor time management: Spent too much time on setup phases, leaving insufficient time for solutions
Excessive verbosity: Frequently over-explained concepts without adding value
Inconsistent pacing: Would speak at length then ask for questions, rather than maintaining steady dialogue with interviewer
Recommended Reading
https://intrico.io/interview-best-practices/suss-a-product-design-product-sense-framework - For better overall structure and time management
https://intrico.io/interview-best-practices/product-sense-strategic-checklist - To improve strategic setup and market timing arguments
https://intrico.io/interview-best-practices/tips-on-thinking-big - For developing more creative and comprehensive solutions
Recommended Number of Mocks Required to Improve
Based on the score of 2, the candidate needs to complete at least 17 more mock interviews before being ready for actual product management interviews.
