LLM Context Compression: Fitting More Into Less
LLM context compression techniques: summarization, retrieval, and token-efficient prompting to fit long histories into small context windows.
Context compression fits long conversations or documents into less space — cutting cost and latency while keeping the information the model needs. It's the practical answer to growing context windows.
Why compress at all
- Longer context costs more per call.
- Latency grows with input length.
- Models focus better on concise, relevant context.
- History accumulates across agent steps.
Compression methods
- Summarization: a model condenses old turns into notes.
- Retrieval: keep only relevant chunks from a corpus.
- Truncation: drop oldest or least relevant turns.
- Structured notes: extract facts into a compact record.
- Token-aware trimming: cut boilerplate and low-value text.
What to preserve when compressing
- User requirements and constraints.
- Decisions already made and why.
- Open questions and next actions.
- Facts the model will need later.
- Errors and dead ends worth avoiding.
Compression is lossy
Summaries can drop nuance. For critical workflows, keep a full log in your system while feeding the model a compressed version — you preserve auditability without paying token costs.
FAQ
What is context compression?+
Reducing the token size of history or documents sent to a model, via summarization, retrieval, or trimming, to cut cost and improve focus.
Does compression hurt quality?+
It can, if important details are dropped. Design summaries to preserve requirements, decisions, and next steps.
When should I compress context?+
When histories grow past a few thousand tokens, in agent loops, and on long-document tasks where cost or latency matters.
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