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AI Product Strategy and Model Choice

AI Feasibility and Technical Spikes Interview Questions

How to find out quickly whether an AI idea can work: scoping a spike, why curated data lies, and how feasibility checks differ for agents. These questions test practical judgement before money is spent.

  • 23 questions with model answers
  • 1 foundational · 11 intermediate · 11 advanced

Foundational AI Feasibility and Technical Spikes Questions

  1. What is a technical spike and how do you scope one for an AI feature?Concept

Intermediate AI Feasibility and Technical Spikes Questions

  1. Design a two-week spike to test whether an AI feature is viable.Case
  2. What question should a feasibility spike answer, and what should it explicitly not try to answer?Concept
  3. How do you prevent a spike from silently becoming the production implementation?Case
  4. What does a good spike report contain?Artifact critique
  5. Explain the difference between technical feasibility and product feasibility.Concept
  6. Describe how you would timebox exploration without killing genuine discovery.Concept
  7. How do you communicate an inconclusive spike result to leadership?Case
  8. Design a spike to determine whether latency requirements can be met.Case
  9. Explain when a manual Wizard of Oz test beats a technical spike.Concept
  10. What is the right team composition for a feasibility spike?Concept
  11. When should you skip the spike and just build?Concept

Advanced AI Feasibility and Technical Spikes Questions

  1. Describe the smallest test that would tell you whether retrieval quality is the blocker.Case
  2. How many examples do you need before a feasibility judgement is trustworthy?Concept
  3. Your spike produces 70 percent accuracy. How do you decide whether that is promising or fatal?Case
  4. How do you run a spike when you do not yet have the data you would need in production?Case
  5. What are the signs a spike is answering the wrong question?Concept
  6. What should you spike first when both data quality and model capability are in doubt?Case
  7. How do you use a spike to de-risk a cost assumption rather than a quality assumption?Case
  8. Describe the decision criteria you would set before the spike begins.Artifact critique
  9. Your spike succeeded on curated data and failed on real data. What did you learn?Case
  10. How does spiking differ for an agent feature versus a single-call feature?Concept
  11. Scope a feasibility spike live for a problem I describe.Interview

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