OpenAI API
The OpenAI API exposes models and tools through interfaces that developers can integrate into their own applications. Competence involves constructing supported requests, managing response and tool lifecycles, and building reliable error, usage and evaluation handling around the selected API rather than treating a generated answer as a complete application.
What it is
For text generation, an application sends instructions and input to a supported endpoint and receives structured response items. Depending on the selected interface and model, a response may contain text, function-call arguments or tool-related events. Streaming allows incremental processing, while conversation handling requires an explicit choice about state and stored items. The API and the ChatGPT product have different application responsibilities. Model selection, permissions, data flow and business logic remain developer decisions even when the service handles model execution or a built-in tool.
What the work involves
A practitioner reads the current endpoint documentation, implements a narrow client adapter and records model identifiers and relevant settings. The adapter handles authentication, deadlines, rate limits, cancellation and incomplete results. Function-call arguments are validated before execution, and externally visible actions are protected against duplicate retries. A representative evaluation suite checks both output quality and application behavior. Useful artifacts include typed request and response handling, an operational usage view and a migration plan that tests a replacement model before releasing it.
Illustrative example
An application reviews customer feedback and emits records containing topic, supporting quotation and uncertainty. It submits the text with a supported structured-output configuration, validates the returned record and checks that the quotation occurs in the input. If the model asks for a function that loads account details, application code applies the account access policy before running it. The resulting workflow combines model inference with deterministic validation and authorization instead of delegating those responsibilities to prompt wording.
Limits and common mistakes
Endpoint names, supported parameters and models can evolve. OpenAI-compatible interfaces from other providers may implement only part of the same contract. Format adherence also does not prove that a field is factually correct. Testing should include unavailable models, interrupted streams and content that cannot be answered from the input. Interface documentation is evidence of supported behavior, not a guarantee that one model will satisfy every task requirement.
Prerequisites
Related skills
- → is an instance of: LLM API Integration
Sources and further reading
- Text generation
Explains generation requests, response handling, model choice and API-specific integration patterns.
- Function calling
Defines the application-managed function call and tool result lifecycle.
Last updated: 2026-10-10