Token estimation for product managers

Use token estimates when roadmap conversations include AI-assisted implementation, LLM features, generated tests, or tickets with large context requirements.

Product managers can use this page when ordinary story-point planning does not capture how much AI-assisted exploration, prompting, generation, and review the work may require.

Ask engineers what the assistant will need to read, how often they expect to iterate, and which tickets may create unusually high AI usage.

In Token Poker, ask participants to vote privately before reveal. A private vote reduces anchoring and makes it easier to see whether disagreement is about delivery effort, likely AI usage, or both.

Record the final story-point estimate, final token estimate, and the assumptions that changed during discussion. The assumptions are often more useful than the number because they show what the team needs to learn next.

If token estimates are high, ask whether the ticket needs extra discovery, smaller slices, clearer acceptance criteria, or a deliberate decision to spend more AI budget on the work.

Product teams should avoid presenting token estimates as exact budgets unless actual usage data and current provider pricing are available.

Competitor and tool comparisons should stay specific. Token Poker focuses on AI-aware planning poker; other tools may be a better fit when a team only needs classic story-point voting or has different workflow requirements.