START Project Documentation Hub

Welcome to the comprehensive documentation for the START (Scalable, Tailored Active-inference Research & Training) project - an advanced AI-powered system for creating personalized Active Inference and Free Energy Principle curricula.

🚀 What is START?

START is a complete educational content generation pipeline that combines:

  • Real-time research using Perplexity API for current domain insights
  • Advanced LLM-based content generation via OpenRouter for professional-quality curricula
  • Comprehensive personalization tailored to specific learners and professional domains
  • Multilingual capabilities with full cultural adaptation
  • Rich visualizations including charts, diagrams, and interactive elements

📚 Documentation Structure

Core Guides

🛠️ Setup & Development

  • Environment Setup - Complete installation, configuration, and development guide
  • Prerequisites, API setup, dependency management
  • Development workflow, testing, troubleshooting
  • IDE integration and advanced configuration

🔄 System Architecture

  • Pipeline Overview - Comprehensive system architecture and workflow
  • acquire → prepare → process → parse → render curriculum pipeline
  • Configuration-driven research approach
  • API integration and content generation standards

🧪 Methods and Publication

🔗 External Integrations

  • Repository & Clone Management - External resource integration
  • Active Inference Institute ecosystem integration
  • Knowledge graph and implementation repositories
  • Educational resource enhancement strategies

Specialized Documentation

📖 User Guides

🔧 Configuration Reference

  • Entity Configuration: data/config/entities.yaml - Target learner profiles
  • Domain Configuration: data/config/domains.yaml - Professional domain definitions
  • Language Configuration: data/config/languages.yaml - Translation targets

📦 Data & Outputs

📊 Visualizations

🌍 Translations

🧭 Docs & Deployment

📝 Prompt Engineering

  • Domain Analysis Templates: data/prompts/research_domain_analysis.md
  • Curriculum Generation Templates: data/prompts/research_domain_curriculum.md
  • Personalization Templates: data/prompts/research_entity.md
  • Translation Framework: data/prompts/translation.md

🎯 Quick Start Paths

For New Users

  1. Environment Setup - Get up and running
  2. Pipeline Overview - Understand the system
  3. Usage Guide (GitHub)

For Developers

  1. Environment Setup - Development environment
  2. API Docs (GitHub)
  3. Tests (GitHub)

For Researchers

  1. Pipeline Overview - Research capabilities
  2. Clone Management - Access research repositories
  3. Configuration Files - Customize research targets

🌐 External Resources

Active Inference Institute Ecosystem

Math & Programming Resources

In-Repo Entry Points

📋 System Capabilities

Research & Analysis

  • Professional Domains: Life sciences, technology, business, healthcare, education, and whatever you prefer.
  • Configurable Target Entities: Political figures, scientists, tech leaders, educators, and other explicitly configured audiences
  • Live Research: Current industry insights and professional analysis when explicitly enabled
  • Comprehensive Analysis: Prompt-targeted domain reports with explicit quality and evidence status

Content Generation

  • Structured Curricula: Prompt-defined learning programs with manifest-backed quality and provenance
  • Personalized Learning: Tailored strategies whose generated structure and evidence status are validated
  • Modular Design: Prompt-defined learning units whose actual scope is recorded for review
  • Assessment Integration: Built-in evaluation and progress tracking

Visualization & Media

  • Data Visualizations: PNG charts with curriculum metrics and analysis
  • Process Diagrams: Mermaid diagrams for structure and flow
  • Interactive Elements: Visual learning aids and conceptual frameworks

Multilingual Support

  • Configured Languages: Language and script mappings are loaded from data/config/languages.yaml
  • Cultural Adaptation: Full localization beyond literal translation
  • Review Boundary: Structural and script checks are automated; language fluency and technical accuracy require human review

🔧 Configuration Overview

Research Configuration

# data/config/entities.yaml
entities:
  - name: "karl_friston"
    category: "scientist"
    priority: "high"

# data/config/domains.yaml
domains:
  - name: "biochemistry"
    category: "life_sciences"
    priority: "high"

Command-Line Interface

# From repository root; installed commands use the locked uv environment
uv sync --all-extras --dev

# Research high-priority entities
uv run start-curriculum --non-interactive --stages entity-research \
  --entity-priority high --json

# Generate domain-specific curricula
uv run start-curriculum --non-interactive --stages domain-research \
  --domains biochemistry --json

# Create multilingual content
uv run start-curriculum --non-interactive --stages translations \
  --languages Spanish French --json

# Inspect generated output contracts and deterministic offline fixtures
uv run start-validate-outputs --check
uv run start-regenerate-offline --output-dir /tmp/start-fixtures --json

📊 Project Structure

START/
├── src/                      # Core system implementation
├── learning/                 # Curriculum creation scripts
├── data/                     # Generated content and configuration
├── docs/                     # Comprehensive documentation
├── tests/                    # Test suite and validation
└── README.md                 # Project overview and quick start

🔄 Development Workflow

Standard Development Cycle

  1. Configure targets in data/config/ YAML files
  2. Run research using domain and entity scripts
  3. Generate curricula with comprehensive content creation
  4. Create visualizations for enhanced learning
  5. Translate content for multilingual accessibility

Quality Assurance

  • Comprehensive testing with pytest and TDD approach
  • Code quality with ruff linting and black formatting
  • API integration testing for Perplexity and OpenRouter
  • Content validation against Active Inference standards

📞 Getting Help

Documentation Resources

  • This documentation hub for comprehensive guides
  • Inline code documentation with detailed docstrings
  • Example usage in test files and usage guides
  • Configuration examples in YAML files

Community & Support

  • Active Inference Institute for research questions
  • GitHub Issues for technical problems and feature requests
  • Test Suite for usage examples and validation patterns

START represents a new paradigm in educational content creation, combining cutting-edge AI research capabilities with comprehensive pedagogical design to produce world-class Active Inference curricula tailored to any professional domain or individual learner.