About this course
Cost optimization is an engineering problem, not a finance problem. The highest-leverage changes — prompt compression, caching, model switching, and retrieval optimization — are decisions engineers make every sprint. This course ranks the changes by effort vs savings, shows you how to evaluate model switches without regressions, and teaches you to measure whether the work actually moved the numbers.
What you will learn
- How to rank optimization tactics by savings vs engineering effort
- When RAG, fine-tuning, or full-context is the right economic choice for your workload
- How to switch to a cheaper model without losing user-facing quality
- How to measure cost improvement by provider, service, and category after rollout
How to use this course: Work through the modules in order for the full picture, or jump to the lesson that matches the problem in front of you right now. Each module is a standalone read — estimated total time is 49 minutes.
Course modules
5 lessons · 49 min total read time
LLM cost optimization playbook
Prioritize prompt compression, caching, smaller models, batching, and retrieval optimization with a clear savings vs effort ranking.
RAG vs fine-tuning cost tradeoffs
Choose RAG, fine-tuning, or full-context for knowledge-heavy or behavior-heavy workloads with a clear cost and maintenance comparison.
Switching to cheaper AI models without losing quality
Use a practical evaluation loop to cut cost while protecting user-facing quality and latency.
Track and attribute AI costs across your stack
Make optimization work measurable by tying spend to providers, services, categories, and projects across your stack.
LLM spend tracking for product teams
Use category-level and service-level analysis to see where optimization work will move margin the most.
More in Optimization