Designing Applications Around Token Constraints
Explore essential strategies for designing LLM applications that handle token constraints effectively. Understand how to use document chunking, summarization chains, sliding windows for conversations, and retrieval-augmented generation to manage large inputs and optimize token usage in real-world AI applications. Gain practical insights into balancing cost, latency, and accuracy while maintaining context.
Token limits are not just a theoretical constraint. They directly shape how you design every component of an LLM-powered application. The previous lesson established that context windows cap the total number of tokens a model can process in a single request, covering both input and output. Now the question becomes practical: what do you do when your data does not fit?
Most real-world inputs blow past even generous context windows. A 200-page legal contract, a multi-file codebase, or a year’s worth of customer support tickets can easily run into hundreds of thousands of tokens. Simply choosing a model with a larger context window is not always the answer. Larger windows increase cost per request, add latency, and research has shown that models suffer from lost-in-the-middle degradation, where information placed in the center of a long prompt is recalled less accurately than information near the beginning or end.
This lesson introduces four complementary strategies that form the standard toolkit for production LLM applications: document chunking, summarization chains, sliding windows, and retrieval-augmented generation. To ground each strategy, consider a concrete scenario. A legal-tech startup needs to analyze 200-page contracts using a model with a 16K-token window. Each strategy offers a different way to bridge that gap, and in practice, production systems combine several of them.
Document chunking
Document chunking is the process of splitting a large document into smaller, self-contained segments that each fit within the token budget. Rather than feeding an entire contract into the model at once, you break it into pieces the model can actually process.
Key design parameters
Three parameters control how chunking behaves, and each involves a trade-off worth understanding.
Chunk size determines how many tokens each segment contains. A common starting point is 300 tokens per ...