Structured output

Use DeepSeek for JSON-style structured output

DeepSeek is useful for extraction, classification, routing, and structured response workflows when you keep prompts tight and validate outputs in your backend.

Structured prompt example

Ask for a compact JSON object, keep output budgets small, and validate the response before using it in production workflows.

curl https://www.gogogoapi.com/api/v1/chat/deepseek \
  -H "Authorization: Bearer $GOGOGOAPI_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "deepseek-v4-flash",
    "messages": [
      {
        "role": "user",
        "content": "Return JSON with keys sentiment and priority for: I need help today."
      }
    ],
    "maxCompletionTokens": 128
  }'

Small outputs fit well

Extraction and classification usually need lower completion budgets than long-form generation.

Backend validation

Parse and validate JSON server-side before trusting the response in downstream systems.

Cost control

Tight prompts and short outputs make structured tasks a good first DeepSeek workload.

Decision guide

Structured output use cases

Use this snapshot to decide whether the route matches your first production or smoke-test use case.

Extraction
Pull fields from tickets or notes

Validate all fields after the model response.

Classification
Sentiment, priority, category

Keep labels constrained for reliable outputs.

Routing
Decide the next internal workflow

Use deterministic backend checks around model output.

FAQ

Questions before you test

Short answers for the questions developers usually ask before creating the first API key.

Does gogogoapi guarantee valid JSON?

No model gateway should be treated as a validator. Ask for JSON, then parse and validate the result in your backend.

Which model should I start with?

Start with deepseek-v4-flash for short extraction and classification tasks.

How do I keep cost predictable?

Constrain the schema, keep prompts short, and set maxCompletionTokens to the smallest useful output budget.

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