How do you handle it when you have more information than the AI can process at once?

Context windows define how much text an LLM can process in a single request. Managing them effectively is crucial for building production AI applications that work with substantial amounts of information.

Understand your model's limits: Context windows range from 4K to 200K+ tokens depending on the model. Longer contexts generally mean higher latency and cost. More context isn't always better—relevant context matters more than quantity.

Chunking strategies for documents: Split text at semantic boundaries (paragraphs, sections) rather than arbitrary character limits. Use overlap between chunks to preserve context. Size chunks appropriately for retrieval—not too small (loses context) or too large (reduces precision).

Summarization and compression: Summarize long documents or conversation histories. Use hierarchical summarization for very long content. Consider using smaller, faster models for summarization before passing to the main model.

Strategic information placement: Important information at the beginning and end of context is typically better retained by models (the "lost in the middle" phenomenon). Put critical instructions in the system prompt. Place retrieved context close to where it's referenced.

Relevance filtering: Use RAG to retrieve only relevant chunks rather than including everything. Implement reranking to prioritize the most relevant content. Filter out low-relevance results rather than stuffing the context.

For conversations: Implement sliding window history, summarize older messages, and maintain key context (user preferences, task state) explicitly. Monitor token usage and implement graceful degradation when approaching limits.