Situation: In AI projects, decisions about model architecture, data curation, or deployment trade-offs can have long-lived consequences. My goal is to make those decisions transparent and discoverable so teammates and future engineers understand why we chose a path and how to revisit it.
Approach (concise):
- Capture decision as close to the moment as possible (when context is fresh).
- Use a lightweight, consistent decision-log (document + link in code repo and issue tracker).
- Record rationale, explicit assumptions, alternatives evaluated, and impact metrics.
- Assign an owner and review cadence so decisions are revisited as data or requirements change.
- Link artifacts (experiments, notebooks, eval results, PRs) so readers can verify evidence.
Sample decision-record template (fields I use):
- Title: short, descriptive
- Date & Author: who decided and when
- Context / Problem: what we’re solving, constraints (cost, latency, privacy)
- Decision: clear statement (e.g., “Use transformer X with distillation for on-device”)
- Rationale: why — evidence, experiments, references to papers
- Assumptions: data distributions, compute budget, expected traffic, regulatory constraints
- Alternatives considered: list with short pros/cons and links to evaluated experiments
- Action items / Implementation plan: owner, tasks, timeline
- Expected metrics / Success criteria: e.g., latency < 50ms, F1 > 0.85, <2% degradation
- Risks & Mitigations: technical debt, bias, maintainability
- Links: experiments, model checkpoints, dashboards, PRs, cost estimates
- Review date: when to re-evaluate and who will do it
Example (brief):
- Title: “Tokenizer change for multilingual corpus”
- Decision: Adopt SentencePiece with 50k vocab
- Rationale: improved OOV handling (+3 BLEU), faster preprocessing in prod
- Assumptions: training data distribution remains similar; memory increase acceptable
- Review date: 6 months or on 10% data drift
This structure keeps decisions actionable, auditable, and easy to revisit—critical for long-lived AI systems where models, data, and constraints evolve.
