As enterprises rapidly adopt large language models (LLMs), managing inference costs has become a critical challenge. Many AI applications repeatedly transmit large amounts of static or redundant context, resulting in unnecessary token consumption, increased latency, and higher operational expenses. While organizations invest heavily in AI capabilities, few optimize how prompts are constructed and delivered to the model.

In this session, Vivek Singh demonstrates practical techniques for reducing LLM token usage by up to 50% through intelligent prompt preprocessing, semantic context selection, and context caching in Google Vertex AI. Attendees will learn how to eliminate redundant information, preprocess structured and unstructured data, retrieve only the most relevant context, and leverage Vertex AI’s context caching capabilities to avoid repeatedly sending identical information.

Through a live demonstration, the audience will compare a traditional “send everything” approach with an optimized pipeline, examining token consumption, response latency, and API costs in real time. The session also covers implementation patterns, architectural best practices, and measurable strategies that can be integrated into enterprise AI applications without sacrificing response quality.

Attendees will leave with the practical knowledge needed to build faster, more scalable, and significantly more cost-efficient generative AI solutions, making this session valuable for AI engineers, data engineers, cloud architects, platform engineers, and developers deploying LLM-powered applications in production.