AI Chat API

An AI chat API accepts conversation messages and returns a language-model response. It enables writing, reasoning, coding and assistant features in your own application. Available input types and controls depend on the selected model and API protocol.

Evaluate models with the prompts, conversation lengths and outputs your product uses. Compare answer quality, instruction following, speed and token cost. Include difficult and unsuccessful examples in the evaluation rather than selecting from one impressive response.

No. Use the model documentation to choose OpenAI-compatible Chat Completions, Claude Messages or Gemini native format. Message structures, authentication details and supported controls can differ. Adapt the request to the documented protocol instead of changing only the model name.

Store the relevant messages in your application and send the history required by the selected protocol. Keep within the model context limit and remove or summarize material that no longer helps the task. A new API request should not be assumed to remember earlier requests automatically.

Check streaming support and the request options for the chosen model and endpoint. If available, handle partial events, completion and errors in your client. Keep a non-streaming path when your workflow needs a complete response before validation or downstream processing.

Only use media types and message structures documented for that model. Some LLM endpoints are text-only and others support multimodal inputs. Check size, format and content limitations before implementing uploads, and test how well the model handles your actual media.

Some models expose structured-output controls; others can only be guided by prompts. Confirm the supported mechanism, supply a clear schema where available and validate the returned data in your application. Do not treat a request for JSON as proof that every response will satisfy your schema.

Only when the endpoint supports the capability and your application provides or enables the required tools. A model can describe a tool call without executing it. Check the documented tool protocol and keep execution, permissions and result handling explicit in your integration.

Provide relevant source material, make the task precise and ask the model to distinguish missing information from known facts. Validate important outputs against trusted data and test recurring failure cases. Model choice and prompting can improve quality, but generated answers still need appropriate review.

Chat pricing commonly separates input and output tokens, and some models have additional tier distinctions. Read the selected model pricing and usage response. Include conversation history in your estimate, since repeatedly sending long context can increase the cost of each turn.

Check the returned error and the selected model limits. Reduce oversized context, control concurrent requests and use bounded retries where appropriate. For a partial streaming response, record what was received and decide how your application should recover instead of silently duplicating a conversation turn.

Open the model page and its API documentation for the correct endpoint, authentication, message body and examples. The model-specific llms.txt resource also identifies the protocol. Keep keys on the server and validate outputs before using them in automated business actions.