Evaluating LLM Performance and Efficiency
By External Provider
About this course
This comprehensive course is for product managers, ML engineers, and technical leads responsible for transforming LLM concepts into reliable, cost-effective production services. In today's AI-driven landscape, building a functional model is only the beginning. You will learn the complete framework for measuring, documenting, and optimizing LLM applications to ensure that they deliver real business value efficiently and consistently. The course begins by grounding you in product-centric development, teaching you to create a clear Product Requirements Document (PRD) that defines scope, MVP features, and success metrics. You'll evaluate features against acceptance criteria to identify gaps and validate user requirements. You will evaluate Zero-Shot, Few-Shot, and Chain-of-Thought prompt patterns and develop runbooks for vector index management. You will learn to analyze compute-spend reports to propose concrete cost-reduction strategies, such as model quantization, and use value-stream mapping to identify and eliminate inefficiencies in your development and release pipelines.
What you will learn
- Create PRDs with requirements and success metrics, and evaluate features against user-story acceptance criteria to identify gaps.
- Evaluate prompt patterns and compute-spend reports to implement model-optimization techniques that reduce operational costs.
- Analyze pipelines using value-stream mapping to eliminate inefficiencies and prioritize chatbot KPI optimizations.
- Create technical documentation for vector index updates and evaluate system effectiveness against business requirements.