Why Compression Matters
Context consumes tokens, and tokens cost money. Beyond direct costs, smaller context payloads mean faster transmission, lower storage costs, and more room for relevant information within model context windows.
Structural Compression
Schema Optimization
Remove unnecessary fields from context payloads. Use short key names in transmission formats. Eliminate redundant nesting levels. Every byte saved multiplies across millions of requests.
Reference-Based Compression
Don't repeat common context. Reference shared context by ID, transmit deltas from baseline contexts, and use dictionaries for frequently-appearing values.
Content Compression
Summarization
Long text context can be summarized while preserving essential information. Use extractive summarization for precision, abstractive for compression. Balance information loss against size reduction.
Deduplication
Identify and remove duplicate context. Hash content for duplicate detection, implement copy-on-write for similar contexts, and deduplicate at storage and transmission layers.
Token-Aware Optimization
Understand how your LLM tokenizes context. Optimize for token efficiency—some representations consume fewer tokens than semantically equivalent alternatives. Profile token usage and optimize hot paths.
