Token optimization is crucial for managing costs and performance in LLM applications. Understanding tokenization, implementing efficient prompting strategies, and leveraging caching techniques can reduce expenses by 50-70% while maintaining output quality.

Understanding Tokenization

• Subword Units: Text is split into tokens, with common words as single tokens and rare words divided into subparts.

• Cost Structure: Most LLM providers charge per token for both input (prompts) and output (completions).

• Token Counting: Tools like tiktoken help accurately estimate costs before API calls.

• Context Windows: Models have maximum token limits (4K, 32K, 128K+) affecting what can be processed.

• Language Differences: Non-English text often requires more tokens per word.

Optimization Strategies

Prompt compression removes unnecessary words while preserving meaning. Dynamic few-shot learning provides examples only when needed. Semantic caching stores results for similar queries to avoid redundant API calls. Request batching combines multiple queries into single calls when possible. Model selection uses smaller models for simple tasks and reserves expensive models for complex ones.

Advanced Techniques

Implement sliding window approaches for long documents rather than processing entire texts. Use summarization for context compression before final processing. Deploy model routing systems that automatically select the most cost-effective model for each request. Monitor usage patterns to identify optimization opportunities and set budget alerts to prevent unexpected costs.

Measuring ROI

Track cost per query, average tokens per request, cache hit rates, and business value delivered per dollar spent. A/B test optimization strategies to quantify improvements without sacrificing quality.