API stands for Application Programming Interface. In AI, an API lets software send a request to a model or service and receive a response. It is the connection used by an application, rather than the model itself or a complete chat interface.
How does an AI API work?
- Your application collects an instruction and the permitted source material.
- It sends a request to a documented endpoint, usually with authentication.
- The service processes the request and returns data, such as generated text or a prediction. Some tasks stream results or finish asynchronously.
- Your application handles errors, checks the result, and decides what to show or do next.
An API can connect to a hosted provider or a locally running service. An application may use an API without displaying a chatbot at all.
Example: draft a support reply
Imagine an application that sends a redacted support ticket and an approved returns policy. The API response contains a proposed reply. The application checks that it follows the policy and shows it to a support agent for review.
| Part | Purpose |
|---|---|
| Request | The task, relevant ticket text, policy, and output format. |
| Authentication | A credential identifying an authorized caller. |
| Response | The returned content or a status/error the application must handle. |
| Review | Check the proposed answer before sending it to a customer. |
This is a system-design illustration, not an API call made by this article.
API versus chatbot versus model
- Model: the trained system that processes input.
- API: a defined interface for software to access a capability.
- Chatbot: an application that presents a conversational interface, often using one or more APIs.
What to check before connecting an API
- Read authentication, supported-model, rate-limit, and error-handling documentation.
- Keep secret API keys on a trusted backend or secret store, not in public browser code.
- Check billing separately from a consumer chat subscription; do not assume one includes the other.
- Check which provider receives each field and what data your organization permits you to send.
- Set spending limits and review any action with an external effect before automating it.
Start with the data-sharing checklist and a small automation workflow. Compare the returned output with the original facts.