Coding Workflows Workflow Intermediate

AI API Design Workflow

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.

The problem

An API designed endpoint-by-endpoint as you code becomes a contract you can't change — clients depend on every accident, and the inconsistencies calcify into a permanent surface you maintain forever. The expensive decisions are made up front: what the resources are, how the endpoints map to them, what the payloads and validation rules look like, how errors and versions behave. 'Ask AI to write the endpoint' skips all of that and hands you the first plausible shape. This workflow designs the API as a contract first — resources, endpoints, payloads, versioning — and reviews it before code makes it permanent.

Recommended workflow

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.

  1. Model the resources and boundaries

    Before endpoints, name the resources the API exposes and where its boundaries sit. Anchor the model as an architect so it reasons about the nouns and their relationships, not the first route that comes to mind.

    Goal The API's resources and boundaries named, before any endpoint is drawn.

    Resource Software Architect Role Prompt
    Tool Role Prompt Generator
    Open this step in Role Prompt Generator

  2. Design the endpoints, payloads, and validation

    Work the design one decision at a time: which endpoints each resource needs, the request and response payloads, the validation rules, and how errors are shaped — consistently across the whole surface, not per-route improvisation.

    Goal Endpoints, payload shapes, and validation rules decided deliberately and consistently.

    Resource Break a Complex Task into Prompt Steps
    Tool Multi-Step Prompt Builder
    Open this step in Multi-Step Prompt Builder

  3. Pin the contract and versioning

    Turn the design into a precise contract — request and response schemas, error formats, and a versioning rule — so clients build against something stable instead of whatever the implementation happens to return.

    Goal A precise request/response contract with a versioning rule, ready to build against.

    Resource Turn a JSON Schema into a Prompt
    Tool JSON Output Prompt Builder
    Open this step in JSON Output Prompt Builder

  4. Review the design before it's code

    Run the design past a review lens — consistency across endpoints, error handling, breaking-change risk, missing validation — while it's still cheap to change, because once clients depend on it the contract is fixed.

    Goal The API design reviewed for consistency and gaps before implementation locks it.

    Resource API Review Checklist Prompt
    Tool Code Review Prompt Generator
    Open this step in Code Review Prompt Generator

Expected outcome

An API designed as a contract before implementation — resources modeled, endpoints and payloads decided, request/response schemas and versioning pinned, and the whole surface reviewed for consistency — so you build against a deliberate design instead of hardening accidents into a contract clients can't escape.

Best for

  • Designing a new API surface before writing endpoints
  • Adding endpoints to an API without breaking its consistency
  • Pinning request/response contracts and a versioning rule up front

Not for

  • Designing the data model behind the API — use the AI Database Design Workflow
  • Deciding the system's overall architecture — use the AI Project Architecture Workflow
  • Documenting an API that already exists — describe the running endpoints instead

FAQ

How is this different from the AI Project Architecture Workflow?

Architecture decides the system's overall structure — services, boundaries, data flow. This zooms in on one surface: the API contract — resources, endpoints, payloads, versioning. You'd usually set the architecture first, then design the APIs that live inside it.

How is this different from the AI Database Design Workflow?

They're two sides of the same build. This designs the interface clients call; database design models the data behind it. The API contract and the schema inform each other, but they're distinct artifacts — the endpoints clients depend on versus the tables they never see.

Does the AI design the API for me?

No. It structures the resource model, surfaces inconsistencies, and pressure-tests the contract so your design is deliberate. The API decisions — and the compatibility promises you make to clients — stay yours.

Part of these blueprints

Complete build journeys that include this workflow as a stage.

Blueprint Build a SaaS MVP with AI 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. View Blueprint → Blueprint Build an API Backend with AI 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. View Blueprint → Blueprint Build an AI Workflow Automation System with AI The full path to automation that survives the real world — wire the integrations and triggers, design the control API, move the data through validated stages, evaluate the AI steps, then deploy. View Blueprint → Blueprint Build a Marketplace with AI The full path to a two-sided platform — define the buyer-and-seller requirements, model the data, design the API, build roles and permissions, wire integrations, design the UI, then test, secure, and ship it. View Blueprint → Blueprint Build a CRM with AI The full path to a CRM that fits your sales process — define the contacts, deals, and pipeline, model the data that ties them together, then build the roles, integrations, and pipeline UI, and ship. View Blueprint → Blueprint Build an E-commerce Store with AI The full path to a store you own end to end — model the catalog and orders, design the storefront and checkout, add customer accounts and payments, then secure it, test it, and ship. View Blueprint → Blueprint Build a Data Pipeline with AI The full path to a pipeline that moves data without corrupting it — design the ingestion and transforms, extract and structure the sources, gate the quality, store it, then deliver and ship it monitored. View Blueprint →

Where to go next

Recommended next workflow AI Database Design Workflow 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. Use when You're about to create or reshape a database and want the entities, constraints, and indexes designed before the migration is written. Start this workflow

Related workflows

Coding Workflows AI Project Architecture Workflow 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. Use when You're designing a new system or feature and need to decide and document the architecture before building it. View Playbook → Coding Workflows AI Integration & Webhook Workflow Connect systems so they don't break each other — map the integration boundaries, design the event and webhook contracts, plan retries and failure handling, then document the integration. Use when You're connecting systems with webhooks or events and need the contracts, retries, and failure handling designed before wiring them. View Playbook →

Tip: Each step's resource opens its tool pre-filled — start at step one and carry the output forward.

All playbooks