Docs & technical writing
AuraScore 83/100

Diffusion Pipeline Endpoint Technical Spec

Document request and response schemas for image generation and inpainting API endpoints.

Use this template when authoring technical API endpoint specifications for cloud-hosted diffusion models and multimodal generation services. It ensures consistency across payload contracts, rate limits, and asynchronous polling schemas.

Template

Role: Senior API Technical Writer and Infrastructure Documentation Architect

Context

  • Microservice Identifier: {{service_name}}
  • Diffusion Pipeline Architecture: {{generation_pipeline}}
  • Supported Schedulers: {{supported_schedulers}}
  • Maximum Batch Size: {{max_batch_size}}
  • Authentication Method: {{auth_scheme}}
  • Production Rate Limits: {{rate_limits}}

Task

Produce an API endpoint specification for {{service_name}} detailing the asynchronous diffusion job lifecycle, payload schema, scheduler parameters, and multimodal asset ingestion contracts.

Method

  1. Document the service endpoint architecture, protocol specs, and security headers governed by {{auth_scheme}}.
  2. Draft the POST request schema for {{generation_pipeline}}, identifying required versus optional parameters.
  3. Define parameter constraints for {{supported_schedulers}} including inference step limits and noise schedule options.
  4. Specify array handling and memory allocation constraints for jobs up to {{max_batch_size}}.
  5. Detail the JSON schema for synchronous responses, webhook callbacks, and polling status endpoints.
  6. Detail HTTP status codes, error payload schemas, and throttling headers enforcing {{rate_limits}}.
  7. Provide concrete JSON payloads representing generation, inpainting, and edge-case failure responses.

Constraints

  • MUST strictly follow OpenAPI 3.1 schema conventions for field types and validation bounds.
  • MUST NOT omit HTTP error code definitions for 400, 401, 422, 429, and 503 states.
  • Payloads MUST include full parameter keys without truncation or placeholder ellipsis.
  • Descriptions must remain technical and implementation-focused.

Output format

  1. Endpoint Overview (Route, HTTP Method, {{auth_scheme}} Requirements)
  2. Request Body Schema (Full JSON Schema definition with parameter boundaries)
  3. Pipeline Controls Table (Schedulers: {{supported_schedulers}}, Batching: {{max_batch_size}})
  4. Response & Polling Contracts (200 OK, 202 Accepted schema)
  5. Error Catalog (Status codes, error types, retry-after headers under {{rate_limits}})

Self-review

  • Validate that all request parameters match the capabilities of {{generation_pipeline}}.
  • Check that batch dimensions correctly cap at {{max_batch_size}}.
  • Verify JSON syntax is valid and fully closed.
AuraScore breakdown
83/100Provisional
Instruction clarity15/15 · Strong

Explicit role, a named task, and discrete steps the model can follow.

Context architecture12/12 · Strong

Background, inputs and variables the model needs before it starts.

Constraint engineering12/12 · Strong

Hard boundaries — what the model must and must not do.

Output specification6/14 · Thin

A named, field-level shape for the response.

Reasoning structure10/10 · Strong

Ordered work items that force analysis before an answer.

Model compatibility10/10 · Strong

Length and structure that travel across frontier models.

Token efficiency7/10 · Adequate

Signal density — instruction weight without padding.

Reusability7/7 · Strong

Documented variables so the scaffold adapts to new inputs.

Robustness3/5 · Adequate

Quality bar, assumptions and behaviour when inputs are thin.

Observed performance1/5 · Thin

How much real usage the template has behind it.

writing-content
writing-docs
image-multimodal-prompting
api-spec
diffusion
image-generation