Hugo Documentation Automation Patterns
Overview
Hugo v0.140+ introduced significant changes to template handling, JSON output, and theme architecture. This page documents proven patterns for automating documentation workflows with Hugo as the core engine.
Key Patterns
1. JSON Feed as API
Hugo’s custom output formats let you generate index.json for any section, turning your static site into a headless CMS:
[outputFormats.JSON]
mediaType = "application/json"
baseName = "index"
isPlainText = true
[outputs]
section = ["HTML", "RSS", "JSON"]
Template (layouts/_default/list.json.json):
LLM-Powered Knowledge Bases
Overview
Large Language Models are transforming how knowledge bases are built, maintained, and queried. This page covers architectures and patterns for AI-powered documentation systems that evolve autonomously.
Architecture Patterns
1. Self-Discovering Documentation
A self-discovering docs site uses AI to:
- Research topics automatically via web search APIs
- Generate structured markdown with proper metadata
- Evolve content on a schedule (daily/weekly via cron)
- Surface new topics through a searchable dashboard
The LLM-Wiki implementation:
Self-Discovering Documentation Systems
Overview
Self-discovering documentation systems automatically identify, research, and integrate new topics without manual curation. They combine web search, AI content generation, and static site generation to create living documentation that evolves over time.
Core Concepts
1. Autonomous Research Loop
Define topics → Web search → Extract insights → Generate markdown → Build → Serve
↑ |
└──────────────────── Schedule (cron) ──────────────────────────────────────┘
The system runs on a schedule (typically daily), researching predefined topics and updating content. New topics can be added by editing the research script’s topic list.