Use Cases + Implementation

How do you build content generation with an OpenAI-compatible API?

Content generation on an OpenAI-compatible API is a pipeline around chat completions: a JSON-mode brief defines audience, structure and required claims; the model drafts; a second pass edits against a rubric; and your application validates links, banned phrases and length before human review. Streaming keeps interactive drafting responsive, and existing OpenAI-based tooling migrates by changing the base URL.

Key facts

EndpointPOST https://api.plugsky.com/v1/chat/completions with streaming for drafting and JSON mode for briefs
CompatibilitySame shape as OpenAI; change base_url and model name
Models30+ models; mix aliases across research, drafting and editing stages
Structured briefsJSON mode returns outline and metadata your pipeline can validate
Pricing modelFlat monthly self-serve plans with unlimited fair-use usage
Free tierFree plan with 2 free AI models, no card required
GovernanceScoped keys and usage analytics per content pipeline
RoadmapImages and batch endpoints are coming soon

TL;DR

  • Make the brief structured data; every downstream stage validates against it.
  • Stream drafts for interactive tools, but persist the complete text for review.
  • Run validators in code: links, banned phrases, length, required sections.
  • Use a separate edit pass against a written rubric, not a vague 'improve this'.
  • OpenAI compatibility means your existing content tooling keeps working.

How it works, step by step

  1. Change the base URL to Plugsky and confirm an existing generation request streams correctly.
  2. Define the brief schema and generate it in JSON mode from a short input.
  3. Draft the body with streaming, requiring references to the sources in the brief.
  4. Run an edit pass that applies the style rubric and is forbidden from adding claims.
  5. Validate programmatically: required sections, word counts, links and banned phrases.
  6. Queue the result for human review and log stage outputs for regression testing.
1Change the base URLto Plugsky andconfirm an existing2Define the briefschema and generateit in JSON mode3Draft the body withstreaming,requiring4Run an edit passthat applies thestyle rubric and is5Validateprogrammatically:required sections,6Queue the resultfor human reviewand log stage

Original data

POST https://aEndpoint30+ models; miModelsFree plan withFree tierSource: Plugsky facts table · updated 2026-09-25

Try it yourself

Open the AI prompt generator →

Architecture of the content pipeline

Four services around one API:

  • Brief builder: JSON mode turns a short request into audience, outline, required claims, sources and constraints.
  • Draft service: streams the body against the brief and source material.
  • Editor service: applies the style rubric, preserving facts and required structure.
  • Validator: code-level checks for links, length, banned phrases and required elements.

Streaming is for the authoring experience; review and validation consume the persisted full text, so the two paths stay independent.

Implementation details

Keep generation reproducible and reviewable:

  • Pass sources in the prompt and require inline references; never rely on model memory for facts.
  • Pin the alias and settings per content type so results are comparable over time.
  • Cap output length per section and assemble, rather than generating one giant response.
  • Keep the edit pass narrow: tone, clarity, structure and length only.
  • Store drafts with model, prompt version, sources and validator results for audit.
  • Reject drafts that fail hard validators automatically instead of sending them to review.

Evaluation and limitations

Score the pipeline with metrics reviewers care about:

  • Brief adherence: required sections and claims present.
  • Rubric score: automated or human scoring of clarity, tone and structure.
  • Edit distance: changes made before publish, tracked per content type.
  • Validator pass rate: share of drafts clearing all code checks on the first attempt.
  • Reviewer rejection reasons: clustered to find recurring prompt or source gaps.

Honest limits: the model cannot verify facts it was not given, image generation and batch processing are coming soon, and originality and licensing review remain human responsibilities.

Honest comparison

CapabilityPipeline on Plugsky APIChat interface copy-pasteTraditional CMS plus freelancers
IntegrationOpenAI-compatible calls in your stackManualEditorial workflow
Brief enforcementStructured brief validated in codeInformalEditor briefs
ValidationAutomated link and length checksManualCopy desk
Model choice30+ aliases across stagesOne interface modelNot applicable
TraceabilityDrafts stored with sources and versionsChat historyDocument history

Frequently asked questions

Do I need two model calls to generate an article?

At minimum a draft pass and an edit pass. Separating them improves structure and prevents the edit step from inventing new claims.

Should I stream the draft?

Stream for interactive authoring, but save the complete text and run validators against the saved version, not the stream.

How do I enforce structure?

Generate the brief in JSON mode with an explicit outline, then validate the draft against that outline in code before review.

How do I keep facts accurate?

Provide sources with the draft request, require inline references, and treat any claim without a source as a defect.

Which model should write versus edit?

Use a stronger alias for drafting and a capable, cheaper one for the edit pass. Different content types may benchmark better on different aliases.

Can Plugsky generate article images?

Not yet — images are coming soon. Keep visual production in your existing tooling for now.

Can I build the pipeline for free?

Yes. The free plan includes two free models with no card, and the 14-day full-access trial lets you benchmark drafting aliases on your briefs.