An AI-generated technical plan can be coherent, detailed, and still unsafe. Fluency makes assumptions look like facts. Ordered steps make dependencies look resolved. A confident conclusion can hide that no rollback condition was ever defined.
Reviewing the plan means slowing down at the points where confidence should be earned: evidence, boundaries, coupling, reversibility, and operational ownership.
Plausibility is not validation
Language models are good at producing familiar plan shapes. A migration plan has phases; a performance plan has measurements and optimizations; an architecture plan has components and tradeoffs. The presence of those sections does not prove the details fit your system.
The learning slope becomes steep when the plan uses concepts you only partly know. Each plausible sentence invites you to accept the next one. Source-level review interrupts that momentum.
Inspect five kinds of risk
Read the plan once for its intended outcome. On the second pass, classify the passages that deserve evidence or a safer alternative.
- Evidence gaps: recommendations made without measurements or system facts.
- Hidden dependencies: teams, services, data, or timing assumed to be available.
- Irreversible steps: destructive changes without a tested recovery path.
- Undefined boundaries: advice that ignores scale, traffic, cost, or failure modes.
- Missing ownership: monitoring and response work assigned to nobody.
Turn concern into an acceptance criterion
“This is risky” identifies a feeling but not the work needed to resolve it. A stronger Comment names what would make the step acceptable: “Use a dual-verification window, define the rollback trigger, and show how existing sessions survive.”
Acceptance criteria let the next AI response revise the plan instead of merely defending it. They also reveal where you need human verification, documentation, or a test before proceeding.
- What evidence supports this recommendation?
- What must already be true for this step to work?
- How do we detect failure before users do?
- What is the rollback trigger and recovery sequence?
- Who owns the system during and after the change?
Preserve useful direction while changing unsafe details
Most plans are not entirely wrong. Mark the goals, constraints, or sequencing that should remain. Then attach Challenges and Concerns only where the plan exceeds its evidence.
The resulting follow-up is easier for the assistant to answer and easier for a human reviewer to audit. It says: keep this goal, verify this assumption, replace this step, and show the revised plan with explicit safety conditions.
Treat an AI technical plan as a draft that has earned attention, not authority. Keep its useful structure, expose its assumptions, and require evidence before execution.