Case study / 06
Structured Content
Automation
A structured content planning and caption-generation workflow for consistent, reviewable publishing.
Type
Content operations
Platform
Workflow system
Status
Built content workflow
Stack
YAML · LLM captions · Validation
Overview
Manual content planning creates inconsistent output, missed schedules, and weak review control.
ArtSports Content OS plans content on a deterministic calendar, compiles prompts from YAML schemas, and generates bounded LLM captions with fallbacks, validation, and retries.
System architecture
Plan
Calendar planning
Schema
YAML structures
Generate
Bounded LLM captions
Validate
Checks + retries
Fallback
Deterministic output
Brief
Daily operator brief
Problem
Content teams need consistency and review control without manual copywriting every day.
What it does
- Deterministic calendar planning
- Prompt compilation
- Bounded LLM captions
- Deterministic fallbacks
- Validation and retries
- Operator-friendly daily briefs
What this proves
- AI-assisted content operations that stay human-reviewable.
- Structured planning before generation.
- Deterministic generation discipline and fallbacks.
- Operator-friendly briefs instead of raw outputs.
Note
The repository is private. This public case study is sanitized; source and client material are not published.