With the explosion of generative AI, developers are increasingly asked to expose data through APIs that AI models will consume. Postman’s latest release—an exhaustive checklist and developer guide—directs teams on how to make those APIs “AI‑ready.” The core message is simple: an AI model can only be as smart as the data it receives, and that data arrives through your endpoints. If your API contracts are fuzzy, endpoints are inconsistent, or error handling is lacking, the model spends precious compute time cleaning up bad inputs instead of producing meaningful insights.
To avoid that waste, Postman recommends a set of best practices that begin with a solid API contract. Use OpenAPI or JSON Schema to define request and response shapes, and enforce versioning so downstream consumers aren’t blindsided by breaking changes. Clear, machine‑readable documentation lets AI pipelines auto‑discover schema and validation rules. Coupled with automated unit and integration tests—run on every commit—and continuous monitoring of latency and error rates, these steps create a predictable data pipeline that AI can trust.
Postman’s toolchain makes implementation straightforward. Create collections that mirror your production endpoints, add mock servers to validate request payloads, and write tests that assert response status codes, data types, and business rules. Use the Monitors feature to schedule recurring checks, and leverage the built‑in version control and CI/CD integrations to enforce the checklist before a new release hits prod. By treating API design as a first‑class citizen in the AI workflow, teams can reduce model training time, lower cost, and deliver higher‑quality AI services.
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