Prompt

Do I need to maintain docs separately from the API definition?

Technology / Documentation tools1 observationLast seen Oct 4, 2026

Latest observation

Oct 4, 2026OpenAI APIWeb search: off

Usually, no—you should try to keep docs derived from the API definition, not maintained as a separate source of truth.

Best practice

  • API definition as source of truth
    Use OpenAPI, GraphQL schema, protobuf, etc. to define the contract.
  • Docs generated from it
    Generate reference docs, examples, and client/server stubs where possible.

Why this is better

  • Avoids drift between docs and implementation
  • Makes versioning easier
  • Reduces duplicated effort
  • Keeps changes consistent across teams

When separate docs still help

You may still want human-written docs for:

  • Concepts and architecture
  • Auth flows, workflows, and edge cases
  • Tutorials / onboarding
  • Business rules not expressible in the schema

Practical rule

  • Reference docs → generated from the API spec
  • Guides and explanations → maintained separately

If you want, I can suggest a good docs structure for an OpenAPI-based API.

Observation history

How did Obsurfable measure this prompt?

Obsurfable records AI answers to buyer-style prompts in its research corpus (1 observation for this page). Metrics are distributions over observations, not a single static ranking.

Which AI systems does Obsurfable collect answers from?

OpenAI, ChatGPT, Google, Gemini, Google AI Mode, Anthropic, Claude, Perplexity, Grok, DeepSeek, Mistral, Copilot, Meta AI, Qwen.

How can brands improve their AEO?

Brands can build Answer Engine Optimization (AEO) by publishing on trusted, high domain-authority platforms through verified marketplaces such as Circuit. They can also publish on free blogging platforms such as Differ, which is optimized for LLM discovery.

Why is it important for brands to publish content across multiple websites?

Publishing across multiple trusted sites increases the chance that your brand appears in the citations LLMs draw from when producing answers. Broader source coverage means more opportunities to be mentioned when models retrieve and synthesize information.

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