LLM API integration

Most products do not host their own model; they rent one. An LLM API lets your code send a request over the internet — your prompt plus some settings — to a provider's servers and get the generated text back, the same way you might call a payments or maps service. You write a few lines, hand over an API key, and a model far larger than anything you could run yourself answers in seconds.

A typical call sends a list of messages — a system instruction, the conversation so far, the new user turn — along with parameters like the model name, temperature, and a maximum token count. The provider returns the completion, usually with a count of input and output tokens, which is what you are billed for. You can stream tokens as they are produced for a responsive feel, ask for structured JSON, or attach tool definitions the model may call. Keys, rate limits, retries, and timeouts are all part of doing this safely at scale.

Renting a hosted model is the fastest path to a capable feature, but it ties you to a vendor's pricing, latency, availability, and content rules, and it sends your users' data to a third party. Production integrations therefore wrap the raw call in defensive code: retry on transient failures, fall back to a second model when one is down, cap spending, and avoid leaking secrets or personal data into prompts.