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.
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Benchmark & Optimize LLM App Performance
Dieser Kurs ist Teil von Spezialisierung für Build Next-Gen LLM Apps with LangChain & LangGraph
Dozenten: Starweaver
Bei enthalten
Empfohlene Erfahrung
Was Sie lernen werden
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.
Kompetenzen, die Sie erwerben
- Kategorie: Retrieval-Augmented Generation
- Kategorie: Responsible AI
- Kategorie: API Design
- Kategorie: Prompt Engineering
- Kategorie: OpenAI API
- Kategorie: A/B Testing
- Kategorie: Application Performance Management
- Kategorie: Model Evaluation
- Kategorie: LLM Application
- Kategorie: Tool Calling
- Kategorie: Performance Tuning
- Kategorie: Performance Testing
- Kategorie: Scalability
Wichtige Details
Zu Ihrem LinkedIn-Profil hinzufügen
Dezember 2025
1 Aufgabe
Erfahren Sie, wie Mitarbeiter führender Unternehmen gefragte Kompetenzen erwerben.
Erweitern Sie Ihre Fachkenntnisse
- Lernen Sie neue Konzepte von Branchenexperten
- Gewinnen Sie ein Grundverständnis bestimmter Themen oder Tools
- Erwerben Sie berufsrelevante Kompetenzen durch praktische Projekte
- Erwerben Sie ein Berufszertifikat zur Vorlage
In diesem Kurs gibt es 3 Module
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.
Das ist alles enthalten
4 Videos2 Lektüren1 peer review
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.
Das ist alles enthalten
3 Videos1 Lektüre1 peer review
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.
Das ist alles enthalten
4 Videos1 Lektüre1 Aufgabe2 peer reviews
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