The Problem
AI can generate code faster than ever. But the real challenge isn't code volume—it's that reviewers don't know where to start without understanding the implementation intent.
When a PR lands with hundreds of lines of AI-generated changes, reviewers face a fundamental question: What was this supposed to do? What decisions were made? What was explicitly out of scope?
What is a "Plan"?
A plan is a compressed decision artifact that makes implementation and review possible. It typically includes:
- Codebase investigation findings — What you learned from exploring the code
- Goals and non-goals — What this change will and won't do
- Relevant specifications — Architecture references, API contracts
- Expected impact and risks — What might break, what needs testing
- Review boundaries — Explicit scope limits
Plans should be "reviewable in size"—not lengthy design documents, but concise enough for practical assessment.
Benefits for Code Review
Intent-First Reviews
Plans shift code review focus from "line-by-line inspection" to validating strategic decisions before examining implementation details. Reviewers know what to look for.
Structural PR Reduction
Explicit review boundaries naturally constrain PR scope. When you document "this change will NOT touch authentication," scope creep becomes visible and preventable.
Quality Enhancement
Human reviewers concentrate on judgment-heavy areas—design decisions, invariants, risk assessment—while AI reviews can verify intent compliance rather than generic code patterns.
Benefits for AI Workflows
- Context compression — Plans reduce token consumption by capturing decisions upfront
- Eliminate repeated scanning — Explicit file lists shortcut codebase discovery
- Enable intent-aware reviews — AI can check code against stated non-goals
Benefits for Team Knowledge
Plans become shareable team knowledge worth versioning. The investigation findings and architectural decisions don't disappear when the PR merges—they persist as documentation of why things are the way they are.
Durable insights can graduate from plans into canonical documentation, creating a sustainable knowledge loop.
The Process Loop
Treating plans as first-class artifacts creates a development loop:
- Commit plans to version control alongside code
- Review intent before reviewing implementation
- Update plans as implementation reveals new decisions
- Promote knowledge into canonical documentation
Plans aren't bureaucracy—they're the missing artifact that makes AI-assisted development reviewable and sustainable.
Originally published on DEV Community