Unlocking Agentic LLM Efficiency: Predicting Costs and Optimizing Workflows

This research explores the exciting challenge of predicting the total cost of Agentic Generative AI Large Language Model workflows, a crucial step toward practical application and cost-effectiveness. The focus on output token count, chain depth, and context growth highlights a forward-thinking approach to optimizing these complex systems. The proposed methods, including regression models and embedding-based cost lookups, offer promising avenues for more efficient Large Language Model utilization.