AI APIs let applications use pretrained models over HTTP without hosting or training anything themselves. A typical integration sends a prompt or input to a provider such as Anthropic, OpenAI, or Google and gets back generated text, structured output, embeddings, or image and speech results. Developers commonly use official SDKs in Python or JavaScript, along with features like streaming responses, tool or function calling, and JSON-schema outputs to connect models to real application logic. Common patterns include chat assistants, document summarization, semantic search with embeddings and vector databases, and retrieval-augmented generation (RAG) to ground answers in your own data. For production use, teams manage API keys securely on the backend, handle rate limits with retries and backoff, cache repeated requests, and track token usage to control cost. Prompt design, output validation, and basic safety checks help keep responses reliable and consistent.

If you share which provider and features you actually used (for example the Claude API, embeddings, or RAG), I can tailor this to your project.