Architecture decisions are the most expensive mistakes you can make in software, and generic AI prompts won't catch them. These ready-to-use Prompt Templates for Architecture Decisions force LLMs to challenge your assumptions instead of agreeing with you.
I've been using these templates with Claude, GPT-4, Gemini, and DeepSeek to pressure-test everything from microservice boundaries to database choices. They work because they turn a vague "review my architecture" request into a structured interrogation. The key difference: they demand trade-off analysis, not just validation.
Why Generic Prompts Fail Here
Ask an LLM "Is my architecture good?" and it will tell you yes. That's the failure mode. These models are trained to be agreeable, and without constraints, they'll rationalize whatever design you present.
Specific problems with generic prompts:
- No context for constraints — the LLM doesn't know your team size, traffic, or timeline, so it gives textbook answers.
- Confirmation bias — the model mirrors your language. If you call something "event-driven," it assumes that's correct.
- Missing trade-off analysis — generic prompts produce a list of pros without weighing them against your actual situation.
The templates below solve this by forcing the LLM to adopt a role, state assumptions, and attack the decision from multiple angles.
Template 1: The Adversarial Review
This template works best for validating a decision you've already made. It forces the LLM to argue against you.
You are a principal architect known for killing bad designs. I'm considering the following architecture decision:
[DESCRIBE THE DECISION IN 3-5 SENTENCES]
Context:
- Team size: [NUMBER] developers
- Current scale: [REQUESTS PER SECOND / USERS]
- Tech stack: [LIST PRIMARY TECHNOLOGIES]
- Business constraint: [TIMELINE / COST / COMPLIANCE]
Your task:
1. Give me the strongest 3 arguments AGAINST this decision.
2. For each argument, rate the severity (Low / Medium / High) and explain why.
3. State the assumption you're making about my context that would make your argument invalid.
4. If you had to pick ONE alternative, what would it be and why?
Be blunt. Do not soften criticism. If the decision is sound, say so explicitly after the critique.
Placeholders explained:
DESCRIBE THE DECISION— be precise. "We're moving from a monolith to microservices" is too vague. Say "We're splitting the billing service into its own deployment because it has different scaling needs."REQUESTS PER SECOND— real numbers matter. The critique changes dramatically between 100 RPS and 100K RPS.TECHNOLOGY STACK— list versions too. "Kafka 3.6" vs "Kafka" changes the answer.
Template 2: The Constraint Matrix
This one is for when you're stuck between two or three viable options and need a structured comparison.
You are a solution architect helping me choose between these options:
Option A: [DESCRIBE OPTION A]
Option B: [DESCRIBE OPTION B]
Option C: [DESCRIBE OPTION C, OR SAY "N/A"]
My constraints, ranked by importance:
1. [MOST IMPORTANT CONSTRAINT — e.g., "time to market under 3 months"]
2. [SECOND CONSTRAINT — e.g., "team has no prior experience with distributed systems"]
3. [THIRD CONSTRAINT — e.g., "must handle 10x traffic spikes during holiday season"]
For each option, provide:
- A score from 1-10 for EACH constraint (not an overall score)
- The single biggest risk if we pick this option
- What would have to be true for this option to become the clear winner
Then, identify which constraint you think I've ranked incorrectly and explain why. Do not recommend an option until you've completed the scoring.
This template works because it forces the LLM to score against your specific constraints, not generic best practices. The last instruction — challenging your ranking — is what separates this from a simple comparison chart.
Template 3: The Post-Mortem Pre-Mortem
For edge cases: you're confident in the decision but want to find the hidden failure modes before they happen.
You are an incident response engineer doing a pre-mortem. It's 12 months from now, and the following architecture decision has failed catastrophically:
[DECISION — e.g., "We adopted a saga pattern for our order processing workflow"]
The failure caused a [SEVERITY LEVEL — e.g., "P0 outage / data loss / security breach"].
Your job:
1. Write the incident report as if it already happened. Include: timeline, root cause, and blast radius.
2. Identify the 3 most likely ways this decision fails, ranked by probability.
3. For each failure mode, tell me what early warning sign I should monitor.
4. Tell me what I should do TODAY to make this failure less likely, ranked by cost/benefit.
Be specific. No "ensure proper testing" — give me concrete mechanisms. If you can't think of a realistic failure mode, say so and explain why this decision is robust.
This template is brutal because it forces the LLM to construct a narrative of failure. It's particularly effective for distributed systems decisions where the failure modes are non-obvious.
How to Adapt These for Your Own Codebase
The templates are skeletons — you need to feed them real context. In my experience, the quality of the output drops sharply if you skip the context section.
Concrete tips:
- Include actual code or config snippets. If you're deciding between two database schemas, paste both. The LLM will catch issues you didn't think to mention.
- State your team's skill level honestly. Saying "we're comfortable with Kubernetes" when you're not will produce useless recommendations.
- Run the same template with different LLMs. I've found Claude is better at the adversarial review (Template 1), while GPT-4 produces stronger constraint matrices (Template 2). Gemini is decent for the pre-mortem. DeepSeek is a solid all-rounder but less opinionated.
- Iterate on the output. The first response is rarely the final answer. Follow up with "Now challenge your own recommendation" or "What did you miss about my context?"
Do These Prompts Work With Any LLM?
Yes, but with caveats. All four major LLMs — Claude, GPT-4, Gemini, and DeepSeek — understand these templates and produce useful output. The differences are in tone and depth.
Claude tends to be more thorough with the adversarial template. GPT-4 gives better structured scoring in the constraint matrix. Gemini is faster but shallower. DeepSeek is a good free alternative that handles all three templates competently, though it's less assertive in its critiques.
One warning: if you're using a smaller or older model, the templates may produce generic filler. The "be blunt" and "rate severity" instructions are ignored by weaker models. Stick with the latest versions of the major models for consistent results.
The one adjustment that improves these prompts the most is adding your actual constraints — team size, timeline, and scale — to every single template. Without that context, even the best prompt produces textbook answers that won't survive contact with your real codebase.