GroveAI
TechnicalFree Template

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

ADR metadata and indexing fields
Context and problem statement section
Options analysis with pros and cons
Decision and rationale documentation
Consequences and trade-off tracking
Review and supersession workflow
1

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Status: Proposed / Accepted / Deprecated / Superseded by ADR-  Date:   Decision makers:   Consulted:  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:  
2

Options Analysis

Options Considered

Option 1: _______________

Description:  

ProsCons
  
  
  

Estimated effort:   person-weeks Estimated cost: £ /month

Option 2: _______________

Description:  

ProsCons
  
  
  

Estimated effort:   person-weeks Estimated cost: £ /month

Option 3: _______________

Description:  

ProsCons
  
  
  

Estimated effort:   person-weeks Estimated cost: £ /month

Comparison Matrix

CriterionWeightOption 1Option 2Option 3
Performance % /5 /5 /5
Cost % /5 /5 /5
Complexity % /5 /5 /5
Maintainability % /5 /5 /5
Team skill fit % /5 /5 /5
Weighted total100%_________
3

Decision & Consequences

Decision

Chosen option:  

Rationale: Explain why this option was selected over the alternatives.



Consequences

Positive Consequences




Negative Consequences (Trade-offs)




Risks Introduced

RiskLikelihoodImpactMitigation
 Low/Med/HighLow/Med/High 
 Low/Med/HighLow/Med/High 

Follow-Up Actions

ActionOwnerDue Date
   
   

Review

Review date:   Reviewed by:   Outcome: Confirmed / Revised / Superseded by ADR- 

Instructions

How to use this template

1

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.

2

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.

3

Evaluate at least 2-3 options

Even if the choice seems obvious, documenting alternatives shows due diligence and helps future teams understand trade-offs.

4

Record consequences honestly

Every decision has trade-offs. Documenting negative consequences is as important as positive ones — it sets expectations.

5

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

Only documenting the chosen option — the rejected alternatives provide crucial context for why the decision was made.
Writing ADRs after the fact — capture decisions while the reasoning is fresh, ideally in the same sprint.
Making ADRs too long — aim for 1-2 pages. If it needs more detail, link to separate technical documents.
Not reviewing ADRs when context changes — schedule periodic reviews and mark outdated ADRs as deprecated or superseded.

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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