AI Content Evolution Strategies
Overview
AI-powered content evolution ensures documentation stays current, accurate, and comprehensive. This page covers strategies for continuously improving content through automated research, quality analysis, and structured updates.
Evolution Strategies
1. Scheduled Research Scans
Run a research script on a cron schedule (daily/weekly) to:
- Search for latest developments in each topic
- Compare new findings against existing content
- Update pages with fresh information and new sources
# Daily at 03:00 via Hermes cron
python3 scripts/research-automation.py
2. Content Freshness Tracking
Each page tracks its own freshness via front matter:
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):
Research Automation System
Overview
The LLM-Wiki features an automated research system that continuously discovers, researches, and evolves content based on current trends and developments.
How It Works
1. Automated Research Pipeline
# Daily research scan
python3 /home/dv/hugo-llm-wiki/scripts/research-automation.py
2. Research Topics
The system researches four key areas:
- Hugo Documentation Automation Patterns: Latest Hugo automation techniques and best practices
- LLM-Powered Knowledge Bases: Advances in AI-powered documentation systems
- Self-Discovering Documentation Systems: Systems that automatically identify and connect related concepts
- AI Content Evolution Strategies: Methods for continuous content improvement
3. Content Generation
Each research topic generates:
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.