AI-Enforced API Design Patterns

🔄 Quick Recall: In the previous lesson, you learned how AI transforms API development by enforcing consistency, generating specs, and maintaining documentation. Now you’ll build the foundational design patterns that every endpoint in your API will follow.

Good API design is about predictability. A developer who learns how one endpoint works should be able to predict how every other endpoint works — the naming, the response structure, the error format, the pagination. AI enforces this predictability by establishing patterns once and reviewing every new endpoint against them.

API Style Guide Generation

AI prompt for API style guide:

Create a comprehensive API style guide for a REST API. Define standards for: (1) URL structure — base path, resource naming (plural nouns, lowercase, kebab-case for multi-word), nesting depth (max 2 levels), (2) HTTP methods — when to use GET/POST/PUT/PATCH/DELETE, idempotency requirements, (3) Request format — JSON body structure, required headers (Content-Type, Accept, Authorization), query parameter conventions, (4) Response format — standard envelope (or no envelope), pagination structure, field naming convention (camelCase/snake_case), (5) Data types — date format (ISO 8601), currency (smallest unit), boolean representation, null handling, ID format, (6) Status codes — which codes for which situations (201 for creation, 204 for deletion, 400 for validation, etc.). Generate the guide as a reference document that can be shared with the team and used for AI-powered design reviews.

Naming convention quick reference:

Resource Modeling

AI prompt for resource modeling:

I’m building an API for [DESCRIBE YOUR DOMAIN — e.g., an e-commerce platform with products, orders, users, reviews, and inventory]. Model the resources and their relationships: (1) identify the core resources (top-level entities), (2) identify sub-resources (entities that only exist in the context of a parent), (3) define the CRUD endpoints for each resource with HTTP methods and URL patterns, (4) specify which fields belong on each resource (required, optional, read-only, write-only), (5) define relationships (1:1, 1:many, many:many) and how to represent them in API responses (embedded, referenced by ID, or linked with URL). Follow REST conventions: plural nouns, 2-level max nesting, and HTTP method semantics.

✅ Quick Check: Your API has orders with order items. A consumer needs to get all items for order #123. Which URL? Option A: GET /order-items?order_id=123. Option B: GET /orders/123/items. (Answer: Both are valid, but B is preferred when items always belong to an order (sub-resource relationship). The URL clearly communicates the hierarchy: items live under orders. Use A when items might be queried independently across all orders. The choice depends on the primary access pattern — AI helps model this by analyzing how consumers will actually use the data.)

Request/Response Schema Design

AI prompt for schema standardization:

Design standard request/response schemas for my API. Create templates for: (1) Single resource response — { "id": "...", "type": "user", "attributes": {...}, "links": {...} } or a simpler flat structure, (2) Collection response — consistent list format with metadata (total count, pagination info), (3) Creation request — what fields to accept, which are required/optional, how to handle defaults, (4) Update request — full replacement (PUT) vs. partial update (PATCH) schemas, (5) Pagination response — cursor-based or offset-based with next/previous links. For each template, generate a concrete JSON example and the OpenAPI schema definition.

Response structure options:

AI-Powered Design Review

AI prompt for endpoint review:

Review this API endpoint design against REST best practices and my style guide. Endpoint: [METHOD] [URL]. Request body: [JSON EXAMPLE]. Response body: [JSON EXAMPLE]. Status codes: [LIST]. Check for: (1) URL naming — does it follow noun-based, plural, kebab-case conventions? (2) HTTP method — is it semantically correct? (3) Request/response consistency — do field names match the established convention? Are data types correct (dates in ISO 8601, etc.)? (4) Missing fields — are there obvious attributes this resource should have? (5) Status codes — are they appropriate for each scenario? (6) Pagination — does the collection endpoint support pagination? (7) Relationships — are related resources properly referenced? Flag issues and suggest fixes.

Key Takeaways

• An API style guide established once and enforced by AI prevents the consistency drift that occurs naturally when multiple developers design endpoints independently — every new endpoint gets reviewed against the same patterns
• Noun-based resource URLs with HTTP method semantics (POST /users, GET /users/:id) are cleaner and more scalable than verb-based URLs (/createUser, /getUser) — AI catches verb patterns and suggests the RESTful equivalent
• Data format standards (ISO 8601 dates, consistent currency units, uniform ID formats) must be defined once in the style guide and enforced everywhere — each inconsistency multiplies across every API consumer
• GET for retrieval, POST for creation — using POST for simple queries breaks caching, logging, and developer expectations. Reserve POST /search for genuinely complex queries that can’t fit in query parameters
• Response structure should be chosen once (flat, envelope, or JSON:API) and applied consistently — AI schema review catches deviations from the chosen pattern before they reach implementation

Up Next

In the next lesson, you’ll learn to generate complete OpenAPI 3.x specifications from plain English requirements — turning design conversations into machine-readable contracts in minutes.