LLM Context Window Management: Strategies for Long Documents
Chunking, hierarchical summarization, and retrieval-augmented approaches
LLM Context Window Management: Strategies for Long Documents
Chunking, hierarchical summarization, and retrieval-augmented approaches
Learn techniques to handle documents longer than LLM context windows including chunking, sliding windows, hierarchical summarization, and retrieval-augmented approaches.
Context window management is critical for LLM applications handling long documents. Key strategies: 1) Fixed-size chunking with overlap (e.g., 1000 tokens with 200 overlap) for simple documents. 2) Semantic chunking using sentence embeddings to detect topic boundaries. 3) Hierarchical summarization for very long documents - summarize chunks recursively. 4) Map-reduce pattern with LangChain for analytical tasks. 5) Dynamic context compression using embeddings to select most relevant chunks for a given query. Token counting with tiktoken is essential before any API call. Cost scales linearly with context - a 100K token GPT-4 call costs $1-3. Always cache intermediate results to avoid redundant expensive API calls.
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