AI-Assisted Product Spec to Engineering Execution

The Challenge

Teams often lose momentum between idea and implementation. A product request may start as a short roadmap note, then spread across tickets, chat, and review comments. Engineers receive incomplete context, and acceptance criteria stay fuzzy until late in the cycle.

This causes predictable issues:

• Story estimates swing because scope is unclear.
• First pull requests solve the wrong problem variant.
• Review cycles focus on requirement interpretation instead of technical quality.
• Delivery dates slip due to repeated clarification loops.

The core problem is not a lack of engineering skill. The problem is translation quality between product intent and execution detail. This use case frames AI as a planning copilot for shaping requirements and producing a build-ready plan before coding starts.

Suggested Workflow

Use a three-pass workflow that separates intent, implementation, and risk validation:

1. Intent pass (ChatGPT / GPT-5): convert a rough feature idea into a structured brief with user outcomes, non-goals, constraints, and measurable success criteria.
2. Execution pass (OpenAI Codex + Cursor): generate a file-level implementation map, task breakdown, and definition-of-done checklist tied to acceptance criteria.
3. Challenge pass (Claude Opus): stress-test assumptions, detect missing edge cases, and mark ambiguous requirements that need human decisions.

The workflow should produce a single planning packet before coding begins:

• problem statement
• acceptance criteria
• files likely to change
• risk register
• test plan

Implementation Blueprint

Create a reusable planning template in the repository and run this sequence for each medium or large feature request:

Input:
- product brief or ticket
- current architecture notes
- non-functional constraints (latency, privacy, compliance)

Output:
1) execution-ready mini spec
2) task graph with dependencies
3) test strategy mapped to acceptance criteria
4) open questions that require product/tech decision

Recommended operating pattern:

• Start with GPT-5 to rewrite feature intent in plain language and list assumptions.
• Use Codex to propose concrete file changes and sequencing.
• Use Claude Code to evaluate repository fit and naming consistency.
• Run Claude Opus as a red-team pass for requirement gaps.

Example planning prompt:

You are planning implementation for an Astro + React + TypeScript project.
Return:
1) acceptance criteria (testable)
2) file-level change plan
3) edge cases and failure modes
4) test matrix (unit/integration/e2e where relevant)
5) rollout and rollback notes
Flag unknowns that need human decision.

Gating checks before implementation:

• Every acceptance criterion must be objectively testable.
• Every critical edge case must have at least one validation step.
• Unknowns must be explicitly assigned to owner roles.

Potential Results & Impact

When teams use this flow consistently, planning quality improves before code is written. The most visible gains usually come from fewer requirement misunderstandings and a smoother path to first pull request.

Track this:

• time from idea intake to execution-ready spec
• number of requirement clarification comments after first PR
• first-PR rework rate tied to missed acceptance criteria
• planning-to-build handoff time
• percentage of tickets with explicit risk and rollback notes

Expected outcome pattern:

• faster transition from product discovery to implementation start
• fewer review comments about missing scope details
• better predictability in sprint commitments

Long-term value compounds as teams reuse the same planning packet format.

Risks & Guardrails

Common failure modes:

• AI output looks complete but hides unstated assumptions.
• Acceptance criteria become verbose but not testable.
• Planning prompts overfit to happy-path behavior.
• Teams skip human alignment because the plan appears polished.

Guardrails:

• Require a human product owner and engineering lead sign-off on the mini spec.
• Mark every assumption with confidence level and owner.
• Add an explicit “what would make this plan wrong?” section.
• Keep one source-of-truth planning doc per feature to prevent drift.
• Treat AI planning output as draft material, never final authority.

Safety practice for high-impact features:

• run a second-model challenge pass before implementation begins
• enforce rollback readiness as part of planning completion, not release day

Tools & Models Referenced

• OpenAI Codex (openai-codex): Converts structured requirements into implementation-oriented plans and task sequencing.
• Cursor (cursor): Helps turn plans into editor-friendly execution checklists and scoped coding tasks.
• Claude Code (claude-code): Repository-aware validation for file conventions and architectural fit.
• GPT-5 (gpt-5): Strong at intent clarification, assumption extraction, and acceptance-criteria drafting.
• GPT-5 Codex (gpt-5-codex): Useful for file-level planning, test scaffolding suggestions, and dependency-aware breakdowns.
• Claude Opus 4.6 (claude-opus-4-6): Independent challenge pass for ambiguity detection and risk-oriented critique.