Architecture Decision Records (ADRs) are one of those things every team agrees they should write and almost no one actually writes. The doc feels bureaucratic, the moment to capture the decision has already passed, and staring at a blank template is its own kind of tax. AI removes most of that friction. Here's a step-by-step walkthrough you can follow right now to produce a solid ADR in under 10 minutes.
What an ADR Actually Needs to Contain
Before prompting anything, know your target. A useful ADR has five sections:
- Title — short, verb-first (e.g., "Use PostgreSQL for transactional storage")
- Status — Proposed / Accepted / Deprecated / Superseded
- Context — what problem forced this decision, what constraints existed
- Decision — what you chose and why
- Consequences — trade-offs, risks, follow-up work
If you can answer those five things out loud, you have everything AI needs.
Step 1: Brain-Dump Your Context
Don't clean up your thoughts first. Paste the raw context directly into the prompt. Messy input is fine — the model's job is to structure it.
I need to write an Architecture Decision Record. Here's the raw context:
We're choosing between Redis and a Postgres-based queue (using SKIP LOCKED)
for our background job system. The team is small, we already run Postgres,
and we want to avoid adding another infra dependency. Redis is faster but
we'd have to operate it separately. We picked Postgres queue. The main
downside is throughput ceiling around ~500 jobs/sec which is fine for now.
Write a complete ADR using this structure:
- Title
- Status: Accepted
- Context
- Decision
- Consequences (separate positive and negative)
Use plain prose, not bullet points inside the sections. Keep it under 300 words.
Step 2: Review the Output Against Your Actual Constraints
AI will produce a clean draft, but it fills gaps with reasonable assumptions. Read it critically for two things:
- Accuracy — did it faithfully represent your constraints, or did it invent ones that sound plausible?
- Specificity — are numbers, system names, and timelines correct?
Fix anything that's wrong before it becomes the permanent record.
Step 3: Stress-Test the Consequences Section
The consequences section is where most ADRs go shallow. Use a follow-up prompt:
Review the Consequences section of this ADR. What risks or trade-offs
are missing? What operational, scaling, or team-knowledge implications
should be added? Be specific, not generic.
[paste your current ADR draft]
Add whatever surfaces that you actually agree with. Discard generic filler.
Step 4: Generate the File and Drop It in Your Repo
ADRs belong in version control, next to the code they govern. Use this prompt to get the final file:
Format the following ADR as a Markdown file ready to commit.
Filename convention: NNNN-short-title.md (I'll supply the number).
Add a "Date" field after Status.
[paste final ADR]
Commit it to docs/decisions/ or wherever your team keeps them. If you don't have a folder yet, create it — this is the first one.
The Workflow at a Glance
| Step | What you do | What AI does |
|---|---|---|
| 1 | Brain-dump raw context | Structures it into ADR format |
| 2 | Review for accuracy | — |
| 3 | Request consequence audit | Surfaces missing trade-offs |
| 4 | Paste final draft | Formats for commit |
The whole thing takes 10 minutes the first time, less after that. The team gets a permanent record of why a decision was made — which is the only thing that matters six months later when someone wants to re-litigate it.
I break down one workflow like this every week in The AI Leverage Weekly — practical, no fluff, free. Subscribe: https://theaileverageweekly.beehiiv.com/subscribe?utm_source=devto&utm_medium=article&utm_campaign=medium_w10
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