Overview

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!

Syllabus

Foundations of LLM Performance & Benchmarks

This module establishes why performance is a product feature, not a backend afterthought. We connect latency, cost, and answer quality to user-perceived speed (p50 vs p95, jitter) and trust. You’ll define a minimal metric set-latency, throughput, tokens/sec, determinism, and win-rate-then build a lightweight benchmarking harness that runs a small eval set, logs prompts/outputs, and exports clean CSVs. By the end, you’ll have a reproducible baseline you can rerun on every change.

Finding & Fixing Bottlenecks: Prompt, Model, and System

In this module, you'll trace where time actually goes: network hops, model inference, prompt bloat, and post-processing. You’ll learn practical prompt patterns that cut tokens without cutting quality, plus schema-first I/O that improves stability and parsing. We’ll add caching strategies for embeddings, RAG retrievals, and tool calls, including cache keys and invalidation rules to avoid stale answers. Expect clear heuristics for cold vs warm paths and a simple checklist to shave seconds-not just milliseconds.

Experimentation at Scale & the Performance Playbook

The final module turns tuning into a disciplined workflow. You’ll run A/B/C tests across model tiers and prompt variants on the same dataset to compare latency, cost per task, and quality with simple stats - then pick a winner. We’ll cover safe scaling: concurrency limits, queues, backpressure, retries, timeouts, and graceful degradation/fallbacks. You’ll leave with a 30-day optimization plan and a production playbook that keeps your app fast, affordable, and reliable after launch.