Reading Real AI API Documentation: Endpoints, Auth, and Rate Limits

Part 14 covered what an AI API is conceptually. Before part 16's actual code, this part covers the real, practical skill of reading a provider's own documentation correctly — the genuine first step of any real integration, not something to skip past to get to code faster.

Reading a real endpoint reference

POST /v1/messages

Real, required parameters:
  model         (string) — which real model to use
  max_tokens    (integer) — the real, maximum tokens to generate
  messages      (array) — the real conversation, per part 11's format

Real, optional parameters:
  temperature   (number, 0-1) — real randomness control (part 13)
  system        (string) — the real system prompt (part 11)
  stream        (boolean) — real, incremental response delivery (part 17)

A real endpoint reference like this tells you exactly what your request needs to contain, and exactly what's optional — worth reading in full before writing a single line of real code, since it's genuinely faster to catch a required-parameter mistake in documentation than through a real, failed API call and its error message.

Reading real authentication documentation

Real, typical documentation pattern:
  "Include your API key in the x-api-key header of every request"

This tells you precisely where the real API key from part 14 actually goes — a genuinely easy detail to get wrong (a different header name, or the wrong authentication scheme) without checking the real, specific documentation for the exact provider being used, since this isn't standardized identically across every API.

Reading real rate limit documentation

Real, typical rate limit structure:
  Requests per minute (RPM): a real cap on how many separate API
    calls can be made in a minute
  Tokens per minute (TPM): a real, separate cap on total tokens
    (input + output combined) processed in a minute

Rate limits are genuinely two separate, real constraints, not one — a real application could stay well under its RPM limit while still hitting its TPM limit, if each individual request is large (a long real conversation history, part 9). Reading both numbers, and understanding which one a specific real application is more likely to hit, is what prevents a genuinely confusing production surprise later.

What happens when a real rate limit is hit

Real, typical behavior: the API returns a specific real HTTP status
  code (commonly 429, "Too Many Requests") rather than processing
  the request

Real, well-written documentation specifies exactly what status code and error format to expect — worth knowing in advance so a real application's error-handling code (covered concretely in part 17) can specifically detect and handle this case, rather than treating every real API failure identically.

A real, practical documentation-reading checklist before writing code

[ ] What's the real base URL and specific endpoint path?
[ ] Which real parameters are required vs. optional?
[ ] Where does the real API key go, and in what header format?
[ ] What are the real RPM and TPM limits for the plan being used?
[ ] What real status code and error format indicates a rate limit
    or other failure?
[ ] What real model identifiers are available, and how do they
    differ in capability and cost?

Running through this real, concrete checklist against a specific provider's actual documentation — genuinely a 10-15 minute real investment — is what part 16's actual first working request builds directly on top of, rather than guessing at request structure and debugging through trial and error.

Next: building your first AI application with Python — real, working code, starting with setup and the first actual API call.