How to document an LLM API so customers trust the meter

Practical patterns for LLM API docs: authentication, rate limits, token billing, error semantics, and clear examples teams can copy-paste.

2026-04

TL;DR

Good LLM API docs cover authentication, rate limits, token billing, error semantics, and copy-paste examples—here are the patterns that work.

Lead with the billing model

State whether you bill per token, per request, or a hybrid. Link to your pricing and lane policy if you blend premium-path and economy usage—buyers want that upfront. See also how teams trim burn without wrecking UX.

Show real requests and responses

Copy-pasteable curl or SDK examples reduce support load. Include error payloads (rate limit, context exceeded, invalid key) with remediation text.

Define limits in tokens, not just characters

Relate maximum prompts and completions to context windows so integrators can size their prompts.

Publish a changelog

Model upgrades and pricing changes should be dated. That builds the same confidence as any serious cloud API.

FAQ

+What should LLM API documentation include?

Effective LLM API docs cover authentication, rate limits, token billing mechanics, error semantics, and clear copy-paste examples that teams can use immediately.

+How should token billing be documented in API docs?

Document input vs output token rates, explain what counts as each, show how system prompts and tool calls affect token usage, and provide cost estimation examples.

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