Evolution System
A systematic approach to evolving the Intelligent Document Generator from a competent rule-based system to an emergent intelligence capable of producing documentation that exceeds human-crafted quality.Vision
The endstate: a system that discovers documentation patterns humans haven’t conceived, while maintaining interpretability and alignment with human values.Overview
The Evolution System implements a multi-phase strategy for iteratively refining documentation generation through:- Instrumentation: Capturing execution telemetry
- Memory: Building searchable corpora
- Analysis: Learning from outcomes
- Optimization: Improving prompts and flows
Evolutionary Phases
| Phase | Objective | Duration |
|---|---|---|
| Phase 1 | Instrumentation & Memory | 2-3 iterations |
| Phase 2 | Pattern Recognition | 3-4 iterations |
| Phase 3 | Prompt Evolution | Ongoing |
| Phase 4 | Architecture Optimization | As needed |
| Phase 5 | Emergent Intelligence | Long-term |
Key Capabilities
Execution Telemetry
Every workflow execution captures:- Input complexity scores
- Generation latencies
- Quality scores across dimensions
- Human feedback signals
Corpus Database
Searchable database of all generated documents for pattern analysis:- Raw inputs and outputs
- Quality metrics
- Human signals (PR approvals, edits, page views)
Weekly Evolution Analysis
Automated analysis producing:- Performance trends
- Pattern discoveries
- Optimization recommendations
Quality Dimensions
| Dimension | Description |
|---|---|
| Technical Accuracy | Correctness of information |
| Clarity & Structure | Readability and organization |
| Completeness | Coverage of topic |
| Audience Fit | Appropriate for target readers |
| Actionability | Practical usefulness |
Human Signal Capture
Post-publication metrics feed back into the system:- PR approval rates and latency
- Post-publish edit frequency
- Page view analytics
- User feedback (thumbs up/down, comments)