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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

# 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 for implementation details.