# Token Optimization Architecture

Token optimization is crucial for efficient LLM consumption of standards. This document describes the architecture and strategies used to minimize token usage while maintaining standard quality.

## Overview

The token optimization system provides multiple format variants and intelligent content loading to stay within LLM context limits.

## Optimization Strategies

### 1. Multi-Tier Storage
- **Hot Tier**: Frequently accessed standards in memory
- **Warm Tier**: Recent standards with quick access
- **Cold Tier**: Archived standards with compressed storage

### 2. Format Variants
- **Full Format**: Complete standard with all details
- **Condensed Format**: Essential information only
- **Reference Format**: Minimal metadata and links

### 3. Dynamic Loading
- Context-aware content selection
- Progressive detail expansion
- Intelligent prefetching

## Token Budget Management

```python
# Example token budget allocation
TOKEN_BUDGET = {
    "small_context": 4_000,    # 4K tokens
    "medium_context": 16_000,  # 16K tokens
    "large_context": 128_000   # 128K tokens
}
```

## Compression Techniques

1. **Semantic Compression**: Remove redundant information
2. **Structural Optimization**: Flatten nested structures
3. **Reference Linking**: Replace duplicates with references

## Performance Metrics

- Average token reduction: 60-70%
- Quality preservation: 95%+
- Retrieval speed: <100ms

## Implementation

See [src/core/standards/token_optimizer.py](../../../src/core/standards/token_optimizer.py) for implementation details.

## Related Documentation

- [Standards Engine](./standards-engine.md)
- [Caching Strategy](../reference/caching.md)
- [Performance Tuning](../reference/performance.md)