Using AI to Run Lightweight Product Discovery

End-of-lesson check

15 questions · take it digitally for instant feedback at tendril.neural-forge.io/learn/quiz/end-ai-product-discovery-final1-adults

1. What kind of work is AI excellent at in discovery?

   1. Replacing customer interviews
   2. Framework-and-synthesis work like trees, mapping, and theme extraction
   3. Picking the winning bet
   4. Closing the sale

2. What artifact can AI build from a vague outcome?

   1. A patent
   2. A sales contract
   3. A structured opportunity solution tree
   4. A demo video

3. What is 'the riskiest assumption' in a proposed solution?

   1. The cheapest assumption
   2. The longest assumption
   3. The newest assumption
   4. The belief that, if wrong, sinks the plan

4. What can AI synthesize from 5–10 interview transcripts?

   1. Themes with supporting quotes
   2. Personal phone numbers
   3. Bank accounts
   4. A celebrity rumor

5. What kind of experiment does AI suggest well?

   1. Multi-million-dollar launches only
   2. Cheap experiments to test specific assumptions
   3. No experiments
   4. Experiments that ignore data

6. What does AI not replace in this workflow?

   1. Drafting an OST
   2. Listing assumptions
   3. Listening to actual customer interviews yourself
   4. Suggesting experiments

7. What does AI not know about what's blocking customers?

   1. Their company name
   2. Their job title
   3. Their basic role
   4. The difference between what they say and what's actually blocking them

8. Which decision must humans make in discovery?

   1. Which opportunity is worth pursuing
   2. Which JSON shape to use
   3. Which framework word to capitalize
   4. Which font to pick

9. What do you miss by reading only AI's interview summaries?

   1. Direct quotes
   2. Tone, hesitation, and offhand comments that turn out to be the real insight
   3. Overall counts
   4. Headers

10. What's the right human practice before trusting synthesis?

    1. Skip the recordings
    2. Trust the AI's first pass
    3. Listen to the recordings yourself
    4. Delete the recordings

11. What's a strong synthesis prompt structure?

    1. 'Summarize'
    2. 'Make me a deck'
    3. 'Be smart'
    4. Paste transcripts; ask for 3–5 themes with the best supporting quote and the riskiest assumption per theme

12. What is an opportunity solution tree?

    1. A structured map from outcome to opportunities to solutions to experiments
    2. A type of code commit
    3. A backend pipeline
    4. A DB index

13. Why is assumption mapping useful?

    1. It costs more
    2. It exposes the load-bearing beliefs your plan rests on
    3. It hides risk
    4. It removes choices

14. What kind of bet does discovery culminate in?

    1. Random selection
    2. An AI-only call
    3. A judgment about which opportunity to pursue, made by humans
    4. A coin flip

15. Which mindset best fits AI in discovery?

    1. AI replaces all discovery
    2. Discovery is impossible with AI
    3. Skip discovery
    4. AI accelerates structuring and synthesis; humans listen, choose, and bet