Content Generation

Content generation without structure produces a blob of text that someone has to manually split into headline, body, and a call to action (CTA). If the model writes a 200-character headline, your page layout breaks. If it forgets the call to action, your marketing page has no conversion path. If the tone drifts from professional to casual mid-paragraph, the brand voice is inconsistent. Wrapping content generation in a schema solves all of this. Each section becomes a separate field with its own length bounds. Tone becomes an enum, not a suggestion. The CTA is a required object with a label and URL. The model fills in the creative content while the schema enforces the structural and editorial constraints.

Goal

Generate a product launch blurb with a headline, subheadline, key points, call to action (CTA), and tone, all bounded and validated for direct web publishing.

Schema contract

{
  "type": "object",
  "properties": {
    "headline": { "type": "string", "minLength": 15, "maxLength": 80 },
    "subheadline": { "type": "string", "minLength": 25, "maxLength": 140 },
    "key_points": {
      "type": "array",
      "minItems": 3,
      "maxItems": 5,
      "items": { "type": "string", "minLength": 20, "maxLength": 140 }
    },
    "cta": {
      "type": "object",
      "properties": {
        "label": { "type": "string", "minLength": 2, "maxLength": 24 },
        "url": { "type": "string", "pattern": "^https://" }
      },
      "required": ["label", "url"],
      "additionalProperties": false
    },
    "tone": { "type": "string", "enum": ["professional", "friendly", "technical"] }
  },
  "required": ["headline", "subheadline", "key_points", "cta", "tone"],
  "additionalProperties": false
}

from typing import Annotated, Literal
from pydantic import BaseModel, ConfigDict, Field

KeyPoint = Annotated[str, Field(min_length=20, max_length=140)]

class Cta(BaseModel):
    model_config = ConfigDict(extra="forbid")
    label: str = Field(..., min_length=2, max_length=24)
    url: str = Field(..., pattern=r"^https://")

class ContentPayload(BaseModel):
    model_config = ConfigDict(extra="forbid")
    headline: str = Field(..., min_length=15, max_length=80)
    subheadline: str = Field(..., min_length=25, max_length=140)
    key_points: list[KeyPoint] = Field(..., min_length=3, max_length=5)
    cta: Cta
    tone: Literal["professional", "friendly", "technical"]

import { z } from "zod";

const contentPayloadSchema = z.object({
  headline: z.string().min(15).max(80),
  subheadline: z.string().min(25).max(140),
  key_points: z.array(z.string().min(20).max(140)).min(3).max(5),
  cta: z.object({
    label: z.string().min(2).max(24),
    url: z.string().regex(/^https:\/\//),
  }).strict(),
  tone: z.enum(["professional", "friendly", "technical"]),
}).strict();

curl https://api.dottxt.ai/v1/chat/completions \
  -H "Authorization: Bearer $DOTTXT_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "openai/gpt-oss-20b",
    "messages": [{ "role": "user", "content": "Announce a new schema audit service for engineering teams.\nTone should be professional and concrete." }],
    "response_format": {
      "type": "json_schema",
      "json_schema": {
        "name": "content_payload",
        "schema": {
          "type": "object",
          "properties": {
            "headline": { "type": "string", "minLength": 15, "maxLength": 80 },
            "subheadline": { "type": "string", "minLength": 25, "maxLength": 140 },
            "key_points": {
              "type": "array",
              "minItems": 3,
              "maxItems": 5,
              "items": { "type": "string", "minLength": 20, "maxLength": 140 }
            },
            "cta": {
              "type": "object",
              "properties": {
                "label": { "type": "string", "minLength": 2, "maxLength": 24 },
                "url": { "type": "string", "pattern": "^https://" }
              },
              "required": ["label", "url"],
              "additionalProperties": false
            },
            "tone": { "type": "string", "enum": ["professional", "friendly", "technical"] }
          },
          "required": ["headline", "subheadline", "key_points", "cta", "tone"],
          "additionalProperties": false
        }
      }
    }
  }'

Example input

Announce a new schema audit service for engineering teams.
Tone should be professional and concrete.

Example output

{
  "headline": "Ship More Reliable Structured Outputs With Schema Audit",
  "subheadline": "Get a concrete review of your JSON Schema constraints, failure modes, and production readiness before rollout.",
  "key_points": [
    "Identify underconstrained fields that lead to invalid or noisy model output.",
    "Receive practical schema rewrites tied to your real production use cases.",
    "Validate compatibility and behavior before launching high-volume workflows."
  ],
  "cta": {
    "label": "Start Schema Audit",
    "url": "https://example.com/audit"
  },
  "tone": "professional"
}

Implementation tips

Tone enum controls voice. "professional", "friendly", "technical": the model adopts the specified style without you embedding tone instructions in the prompt. Add or remove values as your brand guidelines evolve.
Length bounds match your layout. The headline is capped at 80 characters (fits a single line on desktop), the subheadline at 140 (fits two lines), and key points at 140 each. These bounds should come from your actual CSS/layout constraints.
Key points array is bounded. minItems: 3 ensures enough substance for a marketing page. maxItems: 5 prevents the model from listing fifteen points that nobody reads.
CTA is required and structured. The cta object guarantees every generated blurb has a clear action. The pattern: "^https://" on url prevents relative paths, mailto: links, or other formats your publishing system doesn’t support.
Human review is still needed. The schema handles structure and bounds, but brand voice, factual accuracy, and legal compliance still need human eyes. Schema-constrained output makes this review faster because the structure is predictable.

String bounds: control minLength, maxLength, pattern, and format on text fields
Bounded arrays: set min/max item counts on key points, bullet lists, etc.
Enum with fallback: extend tone or category enums with an escape hatch
String reference | Object reference

Fundamentals

Authoring

Patterns and Use Cases

Reference

Goal

Schema contract

Example input

Example output

Implementation tips

​Goal

​Schema contract

​Example input

​Example output

​Implementation tips

​Related docs

Goal

Schema contract

Example input

Example output

Implementation tips

Related docs