Design a system's architecture on its real trade-offs instead of a confident diagram — put the model in an architect's seat, work the decisions one at a time, and write down the why.
Architecture is the set of decisions that are expensive to reverse, and 'ask AI to design it' produces a confident diagram that ignores your constraints, reaches for the trendy option, and is forgotten by next sprint. Designing architecture well is about the trade-offs — where the boundaries go, how data flows, what you optimize for and what you give up — and writing the reasoning down so the next person, usually future-you, knows why. This workflow puts the model in an architect's seat, works the decisions one trade-off at a time, and ends with a document, not a forgotten chat.
Each step uses an existing NewPrompt tool, pre-filled by a matching resource. Open the resource to read it, or jump straight into the tool with the inputs ready.
Put the model in an architect's seat
Anchor the model as a software architect so it reasons about boundaries, data flow, scale, and reversibility — the decisions that are expensive to undo — instead of jumping straight to code.
Work the trade-offs one decision at a time
Break the design into its real decisions — service boundaries, data model, sync versus async, build versus buy — and for each, lay out the options and what each one trades away. One reasoned decision at a time beats a single hand-wave diagram.
Document the decisions and their why
Capture the chosen architecture and, more importantly, the reasoning — the trade-offs accepted, the alternatives rejected — in a document the team can return to when someone asks 'why is it built this way?'
A system architecture decided on its real trade-offs and written down — boundaries, data flow, and technology choices, each with the reasoning behind it — so the build starts from a deliberate design and the team can answer 'why is it this way?' months later.
It produces an architecture document: the chosen boundaries, data flow, and technology choices, each paired with the trade-off it accepts and the alternatives it rejected. Step 3 uses the markdown-output-builder so the reasoning is captured in a file the team returns to, not a forgotten chat.
Work the three steps in order in your own AI tool: anchor the model as a software architect (step 1), then take each design decision one at a time and lay out its options and trade-off (step 2), then document the choices and reasoning (step 3). NewPrompt supplies the prompts and step order; you run them.
Bring the system or feature you're designing, its constraints, and what you're optimizing for — scale, latency, team size, cost. The workflow decides HOW to build; it assumes you already know WHAT. Vague requirements produce vague boundaries, so the sharper your constraints, the better step 2 can weigh each trade-off.
Check each decision against the constraints you set: does the accepted trade-off actually serve what you're optimizing for, and is a rejected alternative quietly a better fit? Pressure-test boundaries and data flow against your real load and team. The AI structures the reasoning; the call and its consequences are yours to own.
Use MVP planning to decide WHAT to build first — the scope. Use this to decide HOW to build it — boundaries, data flow, technology. Different axis: scope versus structure. The usual order is scope the MVP first, then design the architecture for that scope.
No. It structures the trade-offs and pressure-tests the options so your decision is deliberate and recorded. The architecture call — and owning its consequences — stays with you.
Complete build journeys that include this workflow as a stage.
The full path from idea to a shipped SaaS MVP — define and scope the requirements, design the architecture, API, and data model, then build it reviewed, tested, secured, cost-controlled, and deployed.
The full path to a backend you can put clients on — define the requirements, design the architecture, API contract, data model, and access control, then build it reviewed, tested, secured, and shipped.
Ask AI which option to pick and it names one in the first sentence — no criteria, no options laid side by side, its assumptions hidden. Here's how to make it compare the choices against explicit criteria first, then recommend with caveats you can actually check.
In year three someone finds the queue and asks why it's there. Nobody left knows. An ADR is the note that answers them — the constraint that eliminated the alternative, the consequences you accepted on purpose, and what would have to change for the decision to be wrong.
Design an API on its contract instead of discovering it endpoint by endpoint — model the resources, design the endpoints and payloads, pin the contract, then review it before code locks it in.
Design a schema on its data, not a hunch — model the entities and relationships, set the constraints that protect integrity, plan indexes around real queries, then document the schema and migration.
Turn a fuzzy business need into requirements a team can build from — interrogate the need into concrete requirements, shape them as user stories, and write the PRD.