LLM context windows limit how much information models can process in single requests. While context windows have grown substantially, they remain finite, creating challenges for applications working with large documents or long conversations. Effective context management maximizes useful information within constraints while controlling API costs.
Compression Techniques
Several approaches compress information for context efficiency. Extractive summarization identifies and retains key sentences. Abstractive summarization generates concise versions capturing essential meaning. Semantic compression removes redundant information while preserving unique insights. Hierarchical summarization processes large documents in stages, creating layered summaries at different granularity levels.
- Use extractive summarization for factual documents where accuracy is critical
- Apply abstractive summarization for longer narratives requiring coherent synthesis
- Implement progressive compression increasing aggressiveness for older content
- Maintain key entities and dates even in heavily compressed context
- Test compressed context ensuring critical information survives compression
Selective Inclusion Strategies
Not all information deserves equal context priority. Semantic search identifies content most relevant to current queries. Recency-weighted selection favors recent information. Importance scoring ranks content by predicted relevance. Combining these approaches creates dynamic context that adapts to conversation flow while respecting window limitations.
Context Caching
Provider-specific context caching features reduce costs for repeated context. System prompts and reference documents can be cached across requests. Prompt caching particularly benefits applications with large, stable system instructions. Understanding caching limitations and TTLs enables cost-effective context management strategies.