Benchmark & Optimize LLM App Performance

Benchmark & Optimize LLM App Performance is a hands-on journey from “it works” to “it flies.” You’ll start by treating speed and cost as product features-defining a baseline with the right metrics (p50/p95 latency, tokens/sec, throughput, determinism, cost per task) and building a lightweight benchmarking harness you can rerun on every change. Next, you’ll learn to hunt bottlenecks across the stack-network, model, prompt, and post-processing-using practical patterns that cut tokens without cutting quality, plus caching strategies for embeddings, RAG, and tool calls. Then you’ll run A/B/C experiments to compare models and prompts on the same dataset, interpret results with simple stats, and choose a winner confidently. Finally, you’ll harden for production with concurrency limits, queues, timeouts, fallbacks, and a 30-day optimization playbook. Expect reusable templates, clear checklists, and realistic demos designed for busy developers and product builders who want measurable gains-not hype. This course is designed for machine learning engineers, AI developers, data scientists, and product engineers who want to optimize and scale LLM-based applications for production environments. It’s also ideal for backend engineers and DevOps professionals aiming to enhance system performance, reduce latency, and improve cost-efficiency in AI deployments. Additionally, product managers and technical leads overseeing AI-powered systems will benefit from the practical insights provided, helping them to drive improvements in app performance and ensure that their LLM models deliver reliable, high-quality results at scale. This course requires basic knowledge of Python or JavaScript, familiarity with REST APIs, and a high-level understanding of how Large Language Models (LLMs) function. These skills will help you effectively engage with the course content, optimize performance, and implement solutions. By the end of this course, you'll have the skills to optimize LLM performance, tackle real-world bottlenecks, and implement efficient, scalable AI systems. You'll be ready to apply these techniques confidently, making your AI solutions faster, more reliable, and production-ready!

• Optimize LLM behavior using structured prompting and self-checks to reduce variance and errors
• Design scalable middleware to manage API requests, retries, caching, and token budgets for performance targets
• Build user-centered interfaces that collect feedback and improve LLM accuracy and user trust

• Basic familiarity with the topic and its common terminology
• Readiness to practice through applied exercises or case-based work