AI & Technical question
Design a product that analyzes conversations across voice, chat, email, and SMS and gives support teams actionable recommendations (for example, workflow fixes, agent behavior changes, or knowledge-base gaps). What jobs-to-be-done would you prioritize first, what should the UI show so teams trust and act on the recommendations, and how would you handle confidence, evidence/citations, and feedback loops to improve recommendation quality over time?
- Decagon
- AI & Technical
- Hard
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What this question tests
Whether you can design an AI insights product that support teams will actually trust and act on, with concrete answers to confidence, evidence, and feedback loop design, not just a features list.
How to approach it
- Prioritize jobs to be done that are high-frequency and low-risk to act on first, like flagging a recurring knowledge-base gap causing repeated escalations, before higher-stakes recommendations like changing agent policy.
- UI should show the recommendation alongside its evidence directly, for example the specific conversation excerpts that led to the flagged pattern, so the team can verify without hunting for context.
- Show a confidence indicator tied to how many conversations support the pattern and how consistent the signal is across channels, since a recommendation based on three conversations should read differently than one based on three hundred.
- Require explicit citations linking every claim to source conversations across voice, chat, email, and SMS, since cross-channel synthesis without traceable evidence is exactly where trust breaks down.
- Build the feedback loop by letting teams mark a recommendation as acted-on, dismissed, or wrong, feeding that back into how future recommendations of the same type are ranked and surfaced.
What a strong answer includes
- Sequences low-risk, high-frequency recommendations first, matching how trust in AI recommendations is actually built incrementally in production.
- Requires evidence and citations directly in the UI as a design requirement, not an afterthought, directly answering the trust half of the question.
- Defines a concrete feedback mechanism, acted-on versus dismissed versus wrong, that measurably improves recommendation quality over time rather than a generic feedback button.
Common mistakes
- Proposes recommendations with no visible evidence or citation, which is the fastest way to lose support team trust.
- Starts with the highest-stakes recommendation type instead of building trust on lower-risk ones first.
Likely follow-up questions
- How would you handle a recommendation supported by strong evidence in one channel but contradicted in another.
- What would you do if teams consistently dismiss a recommendation type the data says is accurate.
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More questions from Decagon
Learn the skill behind it
Chapters of the AI PM course that teach what this question tests.
- Chapter 1: Foundations: the model and the decisions it forces on you
- Chapter 8: Evals: define good and make the number defensible
- Chapter 6: Agents and agentic architecture