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AI for System Design

5 min read
Tech LeadCto

Software Arch

Use AI to stress-test your design. 'What are the failure modes?' 'How does this scale?' You make the call.

Cloud Arch

AI knows common patterns. Use it for cost/compliance trade-offs. Verify against your actual cloud provider docs.

AI for System Design

TL;DR

  • AI can be a strong thinking partner for architecture: brainstorm options, identify risks, compare trade-offs.
  • It cannot make the final call. It doesn't know your org, team, or constraints.
  • Use it to expand your thinking, not to replace it.

System design is judgment-heavy. AI can't attend your design review. But it can help you prepare, stress-test, and fill gaps before you get there.

What AI Is Good At

  • Generating options — "What are 3 ways to add caching to this system?" You get a starting menu.
  • Trade-off analysis — "Compare event-driven vs. request-response for this use case." Structured pros/cons.
  • Identifying risks — "What are the failure modes of this design?" Often surfaces things you hadn't considered.
  • Pattern recall — "What's the standard pattern for X?" CQRS, saga, circuit breaker, etc.
  • Documentation — "Turn this whiteboard sketch into an architecture doc." Draft, not final.

What AI Can't Do

  • Know your constraints — Team size, budget, legacy systems, compliance, politics. You supply that.
  • Make the decision — "We should use Kafka" vs "We should use SQS" depends on your context. AI can't weigh it.
  • Account for org fit — "We don't have Kafka expertise" — AI doesn't know that.
  • Invent novel solutions — AI recombines known patterns. Truly novel architectures are still human.

A Practical Workflow

  1. Draft your design — Rough architecture, main components, data flow.
  2. Paste into AI — "Here's my design. What am I missing? What are the risks?"
  3. Iterate — "We have to use Postgres, not a new DB." "What changes?"
  4. Stress-test — "How does this fail under load? Under partial outage?"
  5. You decide — Take the feedback. Make the call. Own it.

Prompt Patterns for Design

  • "Compare approach A and B for [goal]. Consider: scale, ops complexity, cost. We're a team of 5."
  • "What are the failure modes of this design? Rank by likelihood and impact."
  • "We're adding [feature] to [existing system]. What's the least disruptive approach?"
  • "Explain the trade-offs of [pattern] (e.g., event sourcing) for our use case: [brief description]."

Common Pitfalls

  • Over-engineering — AI loves to suggest the "right" pattern. Sometimes simple is better. Push back.
  • Outdated advice — Managed services and cloud offerings change. Verify "best practices" against current docs.
  • Ignoring humans — The best design is one your team can build and operate. AI doesn't know your people.

You design a system. You present at the review. Someone asks 'What about failure mode X?' You hadn't thought of it. Rework. Second meeting.

Click "Design → AI stress-test → you decide → review" to see the difference →

Quick Check

AI suggests using Kafka for your event pipeline. Your team has zero Kafka experience. What's the right approach?

Do This Next

  1. Run your next design through AI — Paste a high-level description. Ask for risks and alternatives. See what it surfaces.
  2. Create a "design review" prompt — Template that includes your usual constraints (team size, cloud provider, latency requirements). Reuse it.