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bmad-method

Grooming Fundamentals

Why AI-driven sprints still need refinement

Story grooming in BMAD takes raw stories from the PM phase and makes them genuinely ready for implementation — unambiguous, correctly sized, and free of any interpretation choice the AI would otherwise make silently.

flowchart TD
    RL([Raw Story<br>from PM]) --> GR[Grooming Session]
    GR --> C1{AC covers<br>failure paths?}
    C1 -->|No| FX1[Add failure criteria]
    FX1 --> C1
    C1 -->|Yes| C2{Single-session<br>scope?}
    C2 -->|No| FX2[Split the story]
    FX2 --> C2
    C2 -->|Yes| C3{Dependencies<br>clear?}
    C3 -->|No| FX3[Document prerequisites]
    FX3 --> C3
    C3 -->|Yes| C4{Terms<br>unambiguous?}
    C4 -->|No| FX4[Resolve interpretation]
    FX4 --> C4
    C4 -->|Yes| RD([Ready for<br>implementation])

Grooming is often treated as an optional refinement step. In BMAD it is mandatory, because AI agents do not ask clarifying questions the way a human developer would. A human developer reading an ambiguous story might ask "does this mean X or Y?" before starting. An AI will pick one interpretation and implement it. If it picked the wrong one, the work needs to be redone.

The grooming session takes a story from "written" to "ready." Ready means: the acceptance criteria cover both success and failure paths; the scope is appropriate for a single session; all dependencies are identified and either satisfied or explicitly flagged; and any term in the story that could be interpreted more than one way has been resolved before implementation begins.

BMAD supports several grooming patterns — Party Mode, Sequential Grooming, and Scope Hammering — each suited to different kinds of ambiguity and team contexts. All produce the same output: a story that is unambiguous, correctly sized, and ready for a single-session implementation. The right pattern depends on how much ambiguity the story contains and how many perspectives are needed to resolve it.