This step establishes a structured, repeatable AI workflow that integrates cleanly into your existing development process while preserving reviewability and control.

The core idea is to treat AI as a bounded service, not an autonomous developer. You define:

• What AI is allowed to do (scaffolding, refactoring suggestions, test generation)

• What requires human ownership (security decisions, sensitive data, final approvals)

A standard prompt template ensures consistency. Each prompt includes:

• Clear goal

• Relevant context

• Constraints

• Required output format

• Quality expectations

• Explicit handling of assumptions

Reviewability is enforced through guardrails:

• Small, scoped changes

• Rationale and risk notes

• Test impact analysis

• Structured PR-ready outputs

AI-generated work flows through your normal process:
Branch → AI draft → Local validation → PR → CI → Human review → Merge.

Finally, a reusable context pack (architecture summary, standards, glossary, test conventions, security rules) keeps outputs aligned with system constraints.

Completion Criteria:
You have a documented AI use policy, a prompt template, standard output formats,

a PR-first workflow, and a reusable context pack.

The result is predictable, inspectable AI output that strengthens—not disrupts—your development discipline.

This step focuses on converting vague backlog items into clear, testable user stories that reduce ambiguity and rework.

The central principle:
If a developer cannot immediately derive tests from a story, it is not ready.

Key elements include:

• Defining a precise role, capability, and business value

• Writing behavior-based acceptance criteria using Given/When/Then

• Identifying at least three meaningful edge cases

• Eliminating ambiguity such as undefined actors, hidden rules, or subjective terms

The structured format enforces clarity:

1. Outcome-focused title

2. User story (As a / I want / So that)

3. Behavioral acceptance criteria

4. Explicit edge cases

The result is a backlog item that:

• Drives implementation directly

• Enables immediate test creation

• Surfaces hidden assumptions early

• Minimizes downstream correction cycles

This step shifts stories from “discussion starters” to implementation-ready specifications.