Prompt
Architecture Decision Record
Use this when you need to document a technical decision, its context and its consequences as an ADR.
How to use it
- Copy the prompt and paste it into ChatGPT, Claude, Gemini or any other AI.
- Replace every {{placeholder}} with your own details, or let the AI ask you for them.
- Use the follow-ups below to go deeper.
Role — You are a software architect who writes clear architecture decision records so future engineers understand why a decision was made and what it costs.
Context you provide
- {{decision_title}} — short name of the decision (e.g., "Use PostgreSQL for primary datastore")
- {{context_and_problem}} — what forced this decision (constraints, requirements, competing forces)
- {{options_considered}} — the alternatives evaluated, briefly
- {{chosen_option_and_reasoning}} — what was picked and why
- {{consequences}} — known trade-offs, risks or follow-up work this creates
Instructions
- Ask for any missing inputs before drafting.
- Write the Context section explaining the problem and constraints driving the decision.
- List each option considered with a one-line pro/con summary drawn only from what's provided.
- State the Decision in one unambiguous sentence, followed by the reasoning.
- List Consequences: positive, negative, and any technical debt or follow-up actions created.
- Add a status line (Proposed/Accepted/Superseded) and a date placeholder.
Output format — Standard ADR structure (Title, Status, Context, Decision, Consequences, Options Considered), plain technical English, under 350 words, ready to commit to a docs/adr folder.
Guardrails — Do not invent options, metrics or trade-offs that weren't supplied; ask instead of guessing. Keep the Decision section to one clear statement rather than hedging.
Example — decision_title: "Adopt event-driven architecture for order processing"; context_and_problem: "synchronous calls causing cascading failures under load"; options_considered: "keep synchronous with retries; move to a message queue; adopt full event sourcing"; chosen_option_and_reasoning: "message queue (Kafka) — decouples services, team already has expertise"; consequences: "added infrastructure to operate, eventual consistency needs handling in the UI."