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.
Profitez d'une croissance illimitée avec un an de Coursera Plus pour 199 $ (régulièrement 399 $). Économisez maintenant.
Benchmark & Optimize LLM App Performance
Ce cours fait partie de Spécialisation Build Next-Gen LLM Apps with LangChain & LangGraph
Instructeurs : Starweaver
Inclus avec
Expérience recommandée
Ce que vous apprendrez
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.
Compétences que vous acquerrez
- Catégorie : Performance Testing
- Catégorie : Tool Calling
- Catégorie : Scalability
- Catégorie : A/B Testing
- Catégorie : LLM Application
- Catégorie : Performance Tuning
- Catégorie : Retrieval-Augmented Generation
- Catégorie : Model Evaluation
- Catégorie : API Design
- Catégorie : Application Performance Management
- Catégorie : Prompt Engineering
- Catégorie : OpenAI API
- Catégorie : Responsible AI
Détails à connaître
Ajouter à votre profil LinkedIn
décembre 2025
1 devoir
Découvrez comment les employés des entreprises prestigieuses maîtrisent des compétences recherchées
Élaborez votre expertise du sujet
- Apprenez de nouveaux concepts auprès d'experts du secteur
- Acquérez une compréhension de base d'un sujet ou d'un outil
- Développez des compétences professionnelles avec des projets pratiques
- Obtenez un certificat professionnel partageable
Il y a 3 modules dans ce cours
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.
Inclus
4 vidéos2 lectures1 évaluation par les pairs
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.
Inclus
3 vidéos1 lecture1 évaluation par les pairs
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.
Inclus
4 vidéos1 lecture1 devoir2 évaluations par les pairs
Obtenez un certificat professionnel
Ajoutez ce titre à votre profil LinkedIn, à votre curriculum vitae ou à votre CV. Partagez-le sur les médias sociaux et dans votre évaluation des performances.
Offert par
En savoir plus sur Machine Learning
Pour quelles raisons les étudiants sur Coursera nous choisissent-ils pour leur carrière ?
Foire Aux Questions
To access the course materials, assignments and to earn a Certificate, you will need to purchase the Certificate experience when you enroll in a course. You can try a Free Trial instead, or apply for Financial Aid. The course may offer 'Full Course, No Certificate' instead. This option lets you see all course materials, submit required assessments, and get a final grade. This also means that you will not be able to purchase a Certificate experience.
When you enroll in the course, you get access to all of the courses in the Specialization, and you earn a certificate when you complete the work. Your electronic Certificate will be added to your Accomplishments page - from there, you can print your Certificate or add it to your LinkedIn profile.
Yes. In select learning programs, you can apply for financial aid or a scholarship if you can’t afford the enrollment fee. If fin aid or scholarship is available for your learning program selection, you’ll find a link to apply on the description page.
Plus de questions
Aide financière disponible,