AI Architecture Decision Record Template
A structured template for documenting key architecture decisions in AI projects. Ensures the team captures the context, options evaluated, chosen approach, and expected consequences so future engineers understand why decisions were made.
Overview
What's included
Architecture Decision Record
ADR-[NUMBER]: [TITLE]
Status: Proposed / Accepted / Deprecated / Superseded by ADR- Date: Decision makers: Consulted:
Context
Describe the situation that motivates this decision. What is the problem or opportunity? What constraints exist?
Decision Drivers
- Performance requirements:
- Scalability needs:
- Cost constraints:
- Team expertise:
- Time-to-market:
- Compliance / regulatory:
- Vendor / technology ecosystem:
Options Analysis
Options Considered
Option 1: _______________
Description:
| Pros | Cons |
|---|---|
Estimated effort: person-weeks Estimated cost: £ /month
Option 2: _______________
Description:
| Pros | Cons |
|---|---|
Estimated effort: person-weeks Estimated cost: £ /month
Option 3: _______________
Description:
| Pros | Cons |
|---|---|
Estimated effort: person-weeks Estimated cost: £ /month
Comparison Matrix
| Criterion | Weight | Option 1 | Option 2 | Option 3 |
|---|---|---|---|---|
| Performance | % | /5 | /5 | /5 |
| Cost | % | /5 | /5 | /5 |
| Complexity | % | /5 | /5 | /5 |
| Maintainability | % | /5 | /5 | /5 |
| Team skill fit | % | /5 | /5 | /5 |
| Weighted total | 100% | ___ | ___ | ___ |
Decision & Consequences
Decision
Chosen option:
Rationale: Explain why this option was selected over the alternatives.
Consequences
Positive Consequences
Negative Consequences (Trade-offs)
Risks Introduced
| Risk | Likelihood | Impact | Mitigation |
|---|---|---|---|
| Low/Med/High | Low/Med/High | ||
| Low/Med/High | Low/Med/High |
Follow-Up Actions
| Action | Owner | Due Date |
|---|---|---|
Review
Review date: Reviewed by: Outcome: Confirmed / Revised / Superseded by ADR-
Instructions
How to use this template
Create an ADR for each significant decision
An ADR is warranted for decisions that are hard to reverse, affect system architecture, or have long-term implications. Examples: choosing an LLM provider, selecting a vector database, deciding between batch and real-time inference.
Write the context before the decision
Document the context and constraints first. The context should make sense to someone joining the project in 6 months.
Evaluate at least 2-3 options
Even if the choice seems obvious, documenting alternatives shows due diligence and helps future teams understand trade-offs.
Record consequences honestly
Every decision has trade-offs. Documenting negative consequences is as important as positive ones — it sets expectations.
Store ADRs alongside the code
Keep ADRs in a /docs/adr/ directory in your repository. Number them sequentially (ADR-001, ADR-002) for easy reference.
Watch Out
Common mistakes to avoid
FAQ
Frequently asked questions
An ADR captures a single decision with its context and rationale. A design document covers the full technical design of a system or feature. ADRs are typically shorter (1-2 pages) and focused on one choice point.
A medium-sized AI project typically generates 5-15 ADRs covering decisions like: LLM/model selection, data storage, inference architecture, deployment strategy, monitoring approach, and key integration choices.
ADRs should be reviewed by the people affected by the decision. For major architectural decisions, the tech lead and relevant engineers should review. For decisions with business impact, include the product owner.
Yes. When a decision is reversed, mark the original ADR as 'Superseded by ADR-X' and create a new ADR explaining why the original decision was changed. Never delete or silently edit accepted ADRs.
Common AI ADR topics include: foundation model selection, fine-tuning vs prompt engineering approach, embedding model and vector store choice, RAG architecture, evaluation methodology, data pipeline design, and inference deployment strategy.
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