This is a series on Development using AI. Other articles include making estimates more useful, and the moving from requirements to design.
Architecture decisions shape how a system is built and maintained. Teams may discuss options, reach agreement, and move on — but later, people may not remember why one option was chosen, what alternatives were considered, or which trade-offs were accepted.
An Architecture Decision Record (ADR) preserves that reasoning. The ADR AI skill helps teams turn a decision discussion into a clear, reviewable record. It can organize the context, suggest questions and comparison criteria, structure alternatives, and draft the ADR. The team checks the content, makes the decision, and approves it.
How AI helps create an ADR
The AI skill guides the team through the decision and helps capture:
- The problem or decision being addressed
- The context and constraints that shape the choice
- Alternatives and a consistent basis for comparing them
- The recommendation or approved decision
- Benefits, drawbacks, and expected consequences
- Affected components, owners, implementation notes, and security considerations
- Reviews or approvals that may still be needed
As the team provides information, the AI can identify gaps — such as an option that hasn’t been considered or a consequence that needs further investigation. It can help make the comparison more consistent, but it cannot know whether the options are complete or the trade-offs are acceptable without the team’s system and business context.
The skill also frames the ADR according to its status. A Proposed ADR presents a recommendation for discussion. An Approved ADR records a decision the team has made. It can flag decisions that may need Architecture Review Board (ARB) attention. These prompts and flags help guide review; they do not grant approval.
Why keep the decision in a record?
An ADR gives future team members a way to understand not only what was chosen, but why. It can reduce repeated debates, help reviewers assess whether a proposal fits the system’s needs, and make consequences easier to revisit when requirements or technology change.
AI makes it easier to structure and preserve the discussion. The record is only as reliable as the information the team provides and reviews, so people remain responsible for validating the reasoning and approving the decision.
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