Token Optimization

Token optimization is the practice of deliberately reducing the number of tokens consumed in LLM interactions without meaningfully degrading output quality. Techniques include prompt compression (removing redundant instructions and whitespace), few-shot example pruning, dynamic context selection (retrieving only the most relevant documents in RAG pipelines), output length constraints, and routing simple queries to smaller, cheaper models.

In production commerce AI systems, token optimization is an ongoing engineering discipline rather than a one-time task. As usage scales, even small reductions in average token count per request compound into significant cost and latency savings. A 20% reduction in prompt length across a high-volume product description generation pipeline can translate to material monthly savings and faster response times. Token optimization also interacts with quality: over-pruning context or instructions can degrade accuracy, so optimization work requires systematic evaluation to ensure efficiency gains do not come at an unacceptable quality cost.