How do you implement context window management for long document processing with LLMs?
Context window management enables LLMs to process documents exceeding their token limits. Strategies: (1) Chunking with map-reduce: split document into chunks that fit the context window, process each chunk independently (map), then combine results (reduce). Works for summarization, extraction, and analysis. (2) Iterative refinement: process document in chunks sequentially, carrying forward a running summary that accumulates across chunks. Better coherence than map-reduce but slower. (3) Hierarchical summarization: summarize sections, then summarize the summaries, repeating until a single summary fits the context. Preserves document structure. (4) Smart selection: use embeddings to identify the most relevant portions of the document for a specific query, only passing relevant content to the LLM. Implementation: calculate token counts accurately (use tiktoken for OpenAI, estimate at 4 chars per token for others), reserve space for system prompt, instructions, and output (typically 20-30% of context window), implement overlap between chunks for continuity (10-15% overlap), and track information flow between processing stages. Long-context models: Claude (200K tokens), Gemini 1.5 (up to 2M tokens), and GPT-4o (128K tokens) can process many documents in a single pass. However, long context has limitations: attention dilution (important details in the middle of long contexts may be missed, known as the "lost in the middle" effect), increased cost (more tokens = higher cost), and increased latency. Best practices: even with long-context models, consider using retrieval (RAG) to select the most relevant portions rather than passing everything, place important content at the beginning and end of the context, use clear section markers for document structure, and implement verification steps to catch missed information.