Squeezing the Token: A Lean Framework for LLM Cost and Prompt Optimization
About this session
As generative AI systems transition from prototypes to production, engineering teams face a stark reality: exponential token consumption costs. Under production concurrency, minor prompt inefficiencies and non-optimized contexts compound into massive operational overhead. To scale successfully, builders need a systematic way to predict, calculate, and optimize their token budgets before writing code.
This session introduces a practical framework for LLM cost optimization, centered around an interactive token budgeting and cost-calculation model. Moving past high-level theory, we will unpack the underlying math of token metrics, analyze how slight adjustments in prompt design mathematically compound at scale, and demonstrate how to model cost-efficiency across different LLM tiers.
We will walk through real-world scenarios where unoptimized context windows lead to massive cost spikes, and show how engineers can use structured cost-modeling to systematically prune prompts, predict pipeline expenses, and choose the most cost-effective model routing strategies.
Speaker
Key takeaways
- A clear understanding of token math and the compounding financial impact of prompt design choices under production workloads.
- A structural blueprint for building a token cost-modeling framework to audit and predict application expenses.
- Practical strategies for programmatic context pruning and prompt optimization that can be integrated directly into their deployment pipelines.