After constructing effective prompts, the next practical step is to manage their size. Large Language Models do not have infinite memory; they operate within a fixed input size called the "context window." Sending a prompt that exceeds this limit will result in an error, while inefficiently using the available space can increase both latency and operational costs. API costs are directly tied to token count, often following a model like $Cost = (Price_{input} \times Tokens_{input}) + (Price_{output} \times Tokens_{output})$.

This chapter focuses on the tools and techniques for managing this fundamental constraint. You will learn to:

• Use the tokenizer module to accurately calculate the token count for a piece of text.
• Apply different text truncation strategies to ensure prompts fit within a model's context window.
• Manage a "token budget" for complex prompts that combine multiple sources of information.

By the end of this chapter, you will be able to control your application's token usage, leading to more reliable and cost-effective performance.