CHAPTERS
- 0:00 – 3:25
Why interview take-homes are rising (and why “AI slop” won’t pass anymore)
The hosts frame interview homework as a response to applicant volume and limited recruiting bandwidth. They explain how AI has raised the bar: companies are now screening for real thinking, judgment, and differentiated experience—not generic LLM-generated outputs.
- •Take-homes help companies filter candidates before spending interviewer time
- •Assignments test problem-solving, technical depth, and communication
- •AI makes it easy to generate plausible fluff, so standards are tightening
- •Heuristic: if a junior could produce similar slides, redo the work
- •Differentiation comes from lived experience and original insight
- 3:25 – 8:10
Which companies give PM homework—and where it appears in the loop
They map which types of companies tend to use take-homes (especially AI-native and smaller teams) versus big-tech firms that avoid them for many roles. They also describe how homework can be used at different stages: pre-screen, mid-loop deep dive, or final panel presentation.
- •AI-native/smaller teams often use take-homes to scale evaluation
- •Examples cited: OpenAI/Anthropic-style orgs and other tech-native firms
- •FAANG more often avoids take-homes except some early-career cases
- •Homework can be pre-interview filter, mid-loop deep dive, or final presentation
- •Senior roles may face panel scrutiny on strategy and AI economics
- 8:10 – 10:01
The five common types of PM take-home assignments
Sumant lays out a taxonomy of the most frequent homework formats PMs see. The list spans classic PM outputs (PRDs, strategy, roadmaps) plus the increasingly common AI prototyping requirement for builder roles.
- •PRD from an ambiguous prompt; assumptions and clarification matter
- •Strategy/competition: benchmarking, build vs buy vs integrate decisions
- •Roadmap/prioritization: structured trade-offs and sequencing
- •General case studies: user empathy, product judgment, and taste
- •AI prototyping: increasingly common for builder PM roles
- 10:01 – 12:32
How to use AI effectively: thought partner, process driver, human-in-the-loop
They discuss using LLMs to scaffold thinking rather than write the final deliverable. The recommended approach is decomposed, iterative prompting with evaluation passes (red teaming/sub-agents) and constant human oversight to avoid hallucinations and generic tone.
- •Avoid one-shot “write a PRD” prompts; use step-by-step decomposition
- •Iterate with critiques/red-team style reviews to improve rigor
- •Keep a human-in-the-loop to steer and validate outputs
- •Expect AI-detection/watermarking to make heavy AI-writing obvious
- •Best practice: AI supports reasoning; candidate owns the narrative
- 12:32 – 18:41
Good example #1: taking an LLM incident triage PoC to production
A strong submission is dissected: the candidate clearly separates PoC vs production scope and shows systems thinking. They highlight enterprise-grade considerations like governance, audit trails, phased rollout, real-time ingestion, and bounded AI autonomy with human oversight.
- •Start by contrasting PoC constraints vs production requirements
- •Move from static inputs to real-time event ingestion/APIs
- •Use hard rules above the model; AI provides suggestions, not final decisions
- •Human-in-the-loop for high-stakes actions and mitigation of hallucinations
- •Demonstrates maturity via governance, audit trails, and rollout planning
- 18:41 – 20:17
The “missing 20%”: eval methodology and AI/feature economics
They emphasize that many candidates stop at high-level accuracy claims and never define evaluation approach or cost model. The episode frames evals and economics as the differentiator that moves a submission from ‘good’ to ‘elite.’
- •Most candidates reach ~80% but omit evals and economics
- •Avoid vague accuracy claims; specify evaluation methodology
- •Tie product decisions to unit economics/cost per request and ROI
- •Show business impact with numbers, not just capability statements
- •This rigor is hard to fake with LLMs and signals real experience
- 20:17 – 26:28
Good example #2: unifying five data products by user needs (plus real research)
Another winning take-home shows how to consolidate a messy portfolio by segmenting products around how different user groups consume data. The candidate strengthens credibility with domain experience, primary interviews, and multi-tool secondary research for corroboration.
- •Use a user-centered framework to organize a sprawling product suite
- •Articulate rationale, trade-offs, and opportunity cost (rare but valuable)
- •Primary research: leverage domain contacts to get real user insights
- •Secondary research: run multiple research agents/tools and cross-validate signals
- •Extract insights directly from provided tables/columns (not generic ideas)
- 26:28 – 32:26
Should you do every take-home? Picking the assignments where you can win
They argue for a selective strategy: only invest deeply when you can bring unique ‘alpha’ (domain expertise, access to users, strong fit). If you choose to proceed, commit fully rather than half-measures.
- •Take-homes carry significant opportunity cost in time/energy
- •Select based on fit: can you genuinely differentiate vs other candidates?
- •Domain expertise and user access can create decisive advantage
- •If you opt in, go all-in and reallocate time from weaker opportunities
- •Be explicit about trade-offs in your own job-search strategy
- 32:26 – 36:16
Bad example #1: an “MVP” that tries to do everything (and risky monetization)
A weak submission shows an overloaded MVP feature list without a clear core hypothesis. They also critique a monetization idea involving selling anonymized user data, noting today’s screening (often AI-assisted) will flag such issues quickly.
- •MVP must test a core value proposition—not bundle every idea
- •Over-scoping signals weak prioritization and product judgment
- •Risky ideas (e.g., selling user data) require explicit risk analysis at minimum
- •What passed in 2023 may fail today due to tighter screening and AI review
- •Recruiters look for tough choices, constraints, and thoughtful risk handling
- 36:16 – 39:27
Bad example #2: AI features with no eval plan, metrics, or feasibility depth
They critique a competitive analysis that proposes AI-powered features without specifying data needs, evaluation, cost, or success metrics. The biggest failure is committing to timelines and implementation buzzwords without a plan to measure and control model quality.
- •Feature proposals must include data inputs, quality targets, and success metrics
- •Evals and economics are non-optional for AI features
- •Avoid “AI advertising”: names/pricing/roadmaps without technical/product substance
- •Unrealistic delivery claims without eval gates undermine credibility
- •Demonstrates lack of AI product maturity and execution realism
- 39:27 – 44:21
How to split your limited hours: problem clarity → structure → execution → readability
They provide a practical time-allocation model for a 6–10 hour take-home. The core is to understand the problem deeply first, then outline and justify decisions, and only then polish formatting—while staying on time and avoiding deadline extensions.
- •Spend early hours understanding nuances; don’t rush to solutions
- •Use AI to accelerate research, but keep PM problem-solving central
- •Build a clear outline: assumptions, open questions, trade-offs, prioritization
- •Maintain modest tone; avoid overconfident ‘one right answer’ framing
- •Prioritize readability and basic design hygiene; don’t ask for extra time
- 44:21 – 49:20
Prototypes, constraints, and length limits: when ‘doing more’ backfires
They explain when building a prototype helps (builder roles/agentic products) and when it harms (ignoring the prompt). They stress that constraints like page limits are part of the test; the right workaround is concise output plus optional links—not extra pages.
- •Prototype only if the role/prompt/company context calls for it
- •Unrequested work can signal inability to follow instructions or poor prioritization
- •If you prototype, keep it a thin slice that supports the narrative
- •Strictly follow page/slide constraints; concision is evaluated
- •Use links for optional depth while keeping the official submission within limits
- 49:20 – 59:29
Common homework mistakes and the 3-part system to consistently succeed
They list recurring pitfalls: early-slide typos, answering the wrong prompt, obvious AI-isms, weak use of numbers, and failing to prep a defense. They conclude with a three-part system: understand the problem fast, iterate with rigorous reviews, and prioritize content quality (including evals/economics) over flashy formatting.
- •Avoid first-impression errors: typos/misalignment early in the deck
- •Don’t answer the prompt you wish you received; align to what’s asked
- •Remove AI-isms and generic phrasing; humanize conclusions
- •Use numbers where they strengthen the story; don’t sprinkle them randomly
- •Prep the defense: anticipate challenges, rehearse, and iterate reviews
- •System: (1) understand problem (2) evaluate/review repeatedly (3) focus on content, metrics, evals, and economics
