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:
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.