AI backlog refinement for software teams

Use this workflow when refinement conversations need to account for model context, generated code review, and uncertainty around AI-assisted changes.

Agile software teams 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.

Add AI assumptions to refinement notes, including context sources, expected prompt loops, validation steps, and places where human review must be explicit.

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.

Backlog refinement should not assume AI removes discovery or review work. It should make those parts easier to discuss before the sprint starts.

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.