Architecture Decision Record Author
Produces a rigorous ADR that weighs real alternatives and records consequences, not just the chosen option.
Prompt
ROLE: You are a principal engineer documenting a significant technical decision as an Architecture Decision Record (ADR). CONTEXT: - Decision to be made: [WHAT_WE_ARE_DECIDING] - Forces at play: [REQUIREMENTS, CONSTRAINTS, NON_FUNCTIONAL_NEEDS] - Candidate options being considered: [OPTION_A, OPTION_B, OPTION_C] - Relevant context: [TEAM, TIMELINE, EXISTING_STACK] TASK: 1. State the problem and the forces driving the decision, neutrally. 2. Evaluate each candidate option against the forces, with honest pros and cons (no strawmen). 3. Recommend one option and justify why it best balances the forces. 4. Record the consequences: what becomes easier, what becomes harder, and what we are now committed to. 5. Note what would cause us to revisit this decision. OUTPUT FORMAT (standard ADR): # ADR-[NUMBER]: [TITLE] ## Status: Proposed ## Context ## Decision Drivers ## Considered Options ## Decision Outcome (chosen option + justification) ## Consequences (Positive / Negative / Neutral) ## Revisit Triggers CONSTRAINTS: - Present at least two genuinely viable alternatives with fair treatment. - Distinguish facts from assumptions; mark assumptions explicitly. - Keep it durable: write so a new engineer in 18 months understands why this path was chosen.
How to use this prompt
- 1
Copy the prompt above and paste it into ChatGPT, Claude, or Gemini — or open it in the visual Studio to edit each part on a canvas and run it with your own key.
- 2
Replace any bracketed placeholders with your specifics. The more concrete your context and constraints, the sharper the result — see the 5-part prompt structure.
- 3
Run it, then refine. Ask the model to critique and improve its own answer with self-critique prompting.
Techniques in this prompt
Assigns the model an expert persona so it adopts the right vocabulary, depth, and standards for the task.
Learn this techniqueA tree of thoughts technique used to shape and strengthen the model's response.
Pins the response to a defined structure so it drops straight into your workflow.
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Build on this prompt
Open it in the visual Studio to wire it into a full workflow with your own API key — or learn the craft behind prompts like this.
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