AI RNG: Practical Systems That Ship
A definition of done is not bureaucracy. It is a shared contract that prevents expensive surprises. When teams do not agree on what “done” means, work completes on paper while risk accumulates in reality: missing tests, missing monitoring, silent performance regressions, unclear rollback paths, and security gaps that appear only after release.
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A strong definition of done (DoD) makes delivery safer and faster because it reduces renegotiation. It also makes code review less personal, because the expectations are written down.
This article shows how to build a definition of done that teams actually use, and how AI can help enforce it without turning it into a checklist nobody reads.
What a definition of done should protect
A practical DoD exists to protect a few critical outcomes:
- correctness: the behavior matches the intended contract
- safety: changes can be deployed and rolled back without panic
- operability: you can observe and diagnose the system in production
- maintainability: future changes are easier, not harder
- security and privacy: sensitive data is protected by default
If the DoD does not support these outcomes, it will become performative.
Build the DoD from recurring failure modes
The best DoD is not invented. It is distilled from pain.
Look at your incident history and pull out repeated issues:
- regressions that lacked tests
- incidents that were hard to diagnose due to missing logs
- rollouts that required emergency flag flips
- performance degradations that slipped through
- security findings from unsafe defaults
Each of these becomes a DoD requirement that has a purpose.
Keep it short, but make it specific
A DoD should be short enough to remember and specific enough to enforce.
A compact DoD can be expressed as checks:
| Area | Done means | Evidence |
|---|---|---|
| Behavior | contract is defined and verified | tests or reproducible harness |
| Review | risky changes are highlighted | PR description and checklist |
| Observability | logs and metrics answer likely questions | dashboards or log fields |
| Performance | known hotspots are not worsened | benchmarks or probes |
| Rollout | rollout plan exists for risky changes | feature flag or staged deploy |
| Security | common hazards reviewed | security scan or checklist |
The evidence column matters. It prevents box-checking without proof.
Use AI to generate a DoD draft, then prune ruthlessly
AI can help by taking incident summaries and proposing candidate DoD items. Feed it your recurring failures and ask:
- propose DoD items that would have prevented these failures
- for each item, specify what evidence would satisfy it
- suggest which items can be automated
Then prune. Keep only what you are willing to enforce.
A DoD that is not enforced will be ignored, and ignored rules breed cynicism.
Automate what you can, and keep the human parts meaningful
Automation is how a DoD stays alive.
Automatable items:
- linting and formatting
- unit test execution
- type checks
- secret scanning
- dependency vulnerability scanning
- schema migration checks
- basic performance regression checks
Human judgment items:
- whether the contract statement is clear
- whether the PR description gives reviewers context
- whether rollback risk is understood
- whether monitoring coverage is adequate
If you automate the easy checks, you preserve human attention for the difficult ones.
Integrate DoD into the workflow, not into a document graveyard
A DoD should appear where work happens:
- a PR template with required evidence fields
- CI gates that block merges when missing
- release checklist for risky deployments
- runbook entries that link to the DoD expectations
If the DoD is only a wiki page, it will be forgotten.
A DoD that scales across different kinds of work
Teams often fear that a DoD will not fit every change. The answer is a tiered approach based on risk, without using complicated scoring systems.
You can define change tiers by simple cues:
- low risk: internal refactor with strong tests
- medium risk: behavior change with limited blast radius
- high risk: auth, payments, migrations, large performance impact
Then require more evidence only for higher risk. That keeps the DoD usable without lowering standards on dangerous changes.
A definition of done is how a team makes its values operational. It says: we prefer evidence over confidence, clarity over assumption, and safe delivery over heroics.
Make the DoD readable at review time
A DoD that is hard to apply during code review will be ignored. Translate it into questions reviewers can answer quickly.
Reviewer prompts that map to real risk:
- What is the intended contract change, and where is it documented?
- What tests would fail if the behavior regressed?
- What is the blast radius if this change behaves differently in production?
- What is the rollback plan if a surprise happens?
- What observability was added or updated to explain failures?
AI can help reviewers by scanning a diff and producing a short risk summary, but it should point to concrete evidence: files touched, boundaries crossed, tests added, and configuration implications.
Protect the team from “invisible done”
A common failure is invisible work: changes that appear done because code merged, but are not done because operational safety is missing.
Examples:
- a migration merged without a backout plan
- a new endpoint shipped without auth coverage
- a performance-sensitive path changed without measurement
- a new dependency added without pinning or upgrade plan
A DoD prevents invisible done by requiring evidence in the PR itself.
A practical PR evidence block can include:
| Evidence field | What it contains |
|---|---|
| Intent | why this change exists and what it should do |
| Verification | commands run, tests added, screenshots if relevant |
| Risk notes | known sharp edges, assumptions, failure modes |
| Rollout | feature flag plan or staged deploy notes |
| Observability | logs, metrics, dashboards touched or added |
Keep the DoD alive with periodic pruning
A DoD can become heavy over time. The fix is not to abandon it. The fix is pruning.
- remove items that are never enforced
- split items by risk tier when appropriate
- automate repetitive checks
- update items when incidents show new failure modes
A living DoD feels like a tool that helps the team ship, not a gate that slows the team down.
Keep Exploring AI Systems for Engineering Outcomes
AI Code Review Checklist for Risky Changes
https://ai-rng.com/ai-code-review-checklist-for-risky-changes/
AI Unit Test Generation That Survives Refactors
https://ai-rng.com/ai-unit-test-generation-that-survives-refactors/
AI for Documentation That Stays Accurate
https://ai-rng.com/ai-for-documentation-that-stays-accurate/
AI for Performance Triage: Find the Real Bottleneck
https://ai-rng.com/ai-for-performance-triage-find-the-real-bottleneck/
AI Security Review for Pull Requests
https://ai-rng.com/ai-security-review-for-pull-requests/
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