Interview Questions › AI Product

Launch and price an AI coding assistant

You are launching an AI coding assistant for enterprise teams. Each request costs real money to serve. How would you measure value and set pricing?

Context

Heavy users can cost 20x more to serve than light users. Buyers are engineering leaders who want proof of productivity.

Clarifying questions to ask first

• Who buys and who uses?
• How do we show productivity gains to the buyer?
• What is the cost distribution?

How to approach it

1. Start from the user problem, not the model: is AI the right tool here?
2. Describe the experience and where the model fits in it
3. Define what "good output" means and how you would evaluate it before and after launch
4. Name the failure modes (wrong answers, bias, privacy, cost) and how you contain them
5. Close with a launch plan and success metrics

Framework: User and problem -> Is AI the right tool? -> Experience design and human-in-the-loop -> Quality bar and evaluation -> Risks, cost and latency -> Staged launch and metrics

Sample answer

• Measure retained code, not just accepted suggestions
• Per-seat price with fair-use limits or usage tiers
• Admin dashboard proving value

1. Acceptance rate and share of accepted code still present after 30 days
2. Controlled study of task completion time
3. Cost per seat by usage decile

Prove value with retained-code and task-time studies shown in an admin dashboard. Price per seat with a fair-use cap and a premium tier for heavy users so margin stays positive across deciles.

What interviewers look for

• Accepted suggestions
• Developer productivity
• Gross margin per seat