Architecture Prompts

7 Prompts · Copy-Paste Ready

Home › Vibe Coding Prompts › Architecture

Category · Architecture

Architecture Prompts

7 prompts that force AI to explain tradeoffs, not just recommend a stack. For decisions with long-term consequences.

When to use these: Before you commit to a data model, a framework, a deployment strategy, or a migration path. Not for questions AI can answer with code — those go to the debugging and refactoring prompts.

1

Compare two technical approaches

Use when you're choosing between two viable options. Forces a real comparison instead of "both are fine."

I'm choosing between two approaches for [the problem]. Approach A: [description] Approach B: [description] Constraints: - Scale: [users, requests/sec, data size] - Team size: [N developers] - Timeline: [weeks/months] - Budget: [constraints] - Existing stack: [what we already use] Compare them on: 1. Time to ship the first version 2. Time to ship change #50 (long-term velocity) 3. Operational complexity 4. Cost at scale 5. Hiring/dependency risk 6. Failure modes Then recommend one — and tell me what would have to be true for the other to win.
What you should expect

A comparison table plus a recommendation. The "what would have to be true" line is the real value — it exposes what AI is optimizing for.

2

Design a database schema

Use when starting a new feature or domain. AI's first draft is a good starting point, then you refine based on domain knowledge.

Design a database schema for [feature/domain]. Requirements: - [list the core entities and their attributes] - Expected scale: [rows, growth rate] - Query patterns: [what the app reads/writes most] - Database: [PostgreSQL, MySQL, MongoDB, etc.] Deliverables: 1. Table definitions with columns, types, and constraints 2. Indexes and why each is needed 3. Foreign keys and cascade behavior 4. Unique constraints for data integrity 5. Notes on how the schema handles the top 3 query patterns Also flag: - Denormalization opportunities for read-heavy queries - Potential migration pain points - What you'd need to know to be more confident
What you should expect

A complete schema ready for review. The "what you'd need to know" section is honest — this is where AI admits its uncertainty.

3

Plan a safe migration

Use when migrating databases, frameworks, or infrastructure. AI is excellent at breaking large migrations into safe increments.

I need to migrate from [current state] to [target state]. Current scale: [data size, traffic, users] Downtime tolerance: [zero / X minutes / planned window] Team size: [N developers] Design the migration plan: 1. Break it into safe increments (each one shippable) 2. For each step: - What changes - How it's tested - How it's rolled back - Estimated duration 3. Identify the riskiest step and why 4. What should be monitored during the migration 5. How we know when it's complete Assume we need to keep the current system running during the migration.
What you should expect

A step-by-step plan with rollback for each step. If AI doesn't include rollback, push back: "What's the rollback for each step?"

4

Choose a state management strategy

Use when building a frontend app and unsure where state should live. AI knows the common patterns and their tradeoffs.

I'm building [app description] with [framework]. The app has: - [list state types: user session, form data, cached API, UI state, etc.] - [number of components] - [real-time requirements, if any] Recommend a state management strategy: 1. What goes in local component state 2. What goes in global state 3. What goes in the URL 4. What comes from the server (cached) 5. Which library (if any) — and why For each category, explain the boundary: "X goes here because..." Also: at what size does this strategy need to change?
What you should expect

A layered strategy with clear boundaries. If AI recommends a heavy library for a small app, push back: "What's the simplest option that works at this scale?"

5

Design the API contract

Use when defining endpoints for a new service. AI writes thorough OpenAPI-style specs faster than you can.

Design a REST API for [feature/service]. Consumers: [web app, mobile, third-party, etc.] Auth: [JWT, API key, OAuth, none] Constraints: [rate limits, pagination needs, versioning] For each endpoint: - HTTP method and path - Request body shape (with types) - Response shape (success and error) - Status codes used - Auth requirements - Rate limits Then: 1. Design the error response format 2. Design the pagination pattern 3. Flag any endpoints that feel awkward and propose alternatives 4. List what's NOT in v1 (and why)
What you should expect

A complete API spec with error handling and pagination. The "what's NOT in v1" section is what experienced API designers add — AI mimics that well here.

6

Estimate complexity and identify risks

Use when scoping a feature or sprint. AI is good at breaking down work and flagging unknowns you'd miss.

Estimate the work for this feature: [feature description] Context: - Codebase: [stack, size, age] - Team experience with this stack: [strong / moderate / new] - Similar features we've built: [examples, if any] Break it down: 1. List every task (be specific — not "build UI" but "create form component with X fields") 2. Estimate each task (S/M/L, not hours) 3. Identify dependencies between tasks 4. Flag the 3 riskiest assumptions 5. What's most likely to take 3× longer than expected? Then give me a total estimate with a confidence range.
What you should expect

A task list with risk flags. The "3× longer" question usually reveals the real bottleneck — which is rarely the code itself.

7

Find the failure mode before it happens

Use before shipping a critical system. AI thinks through failure scenarios that you might miss when you're close to the code.

What could go wrong with this system? [describe the system, or paste key files] Expected scale: [users, data, requests] Failure tolerance: [what's acceptable] Think through: 1. Top 3 things most likely to break under load 2. Single points of failure (if any) 3. Data corruption scenarios 4. Cascading failures 5. Security failure modes 6. What happens if a dependency goes down For each failure mode: - Likelihood (low / medium / high) - Impact (if it happens) - Detection (how we'd know) - Mitigation (what to do now vs later) Then: rank all failure modes by (likelihood × impact).
What you should expect

A ranked list of failure modes. The value is the ranking — it turns vague worry into a prioritized action list.

🎯 How to get real architecture advice from AI

❓ Frequently asked questions

Can AI help me design a system architecture?

Yes, for bounded design questions with clear constraints. AI is good at comparing approaches, identifying tradeoffs, and flagging risks. It's not good at understanding team dynamics, budget constraints, or long-term business strategy.

What should I include in an architecture prompt?

Constraints first: scale, budget, team size, existing stack, timeline. Then the specific decision. AI gives better answers when the constraints are explicit — general architecture questions get general answers.

How do I make AI explain tradeoffs instead of just recommending?

Ask it explicitly: 'For each option, list what it optimizes for and what it sacrifices.' Or: 'What would have to be true for the other option to be better?' This forces comparative reasoning.

Can AI design a database schema?

Yes for common patterns (users, posts, orders, etc.). It's weaker on domain-specific schemas where you understand the business better. Use AI's first draft as a starting point, then refine based on your domain knowledge.

Is AI-generated architecture always safe?

No. AI suggestions reflect common patterns, not your specific constraints. Treat its output as one opinion among many. The decisions with long-term consequences still need human judgment.

🔗 Related

🐛 Debugging Prompts
When architecture fails in production.
🔧 Refactoring Prompts
Act on architecture feedback.
🔍 Code Review Prompts
Review new architecture code.
📋 All Prompts
Back to library.
← All Prompts