Optimize & Interface LLM Apps Effectively

О курсе

Ever wondered why your AI app sometimes “sounds smart” but fails when it matters? This course teaches you how to turn unpredictable Large Language Model (LLM) behavior into reliable, production-ready performance.This course is a fast, hands-on journey from prompt to production. You’ll learn to transform vague model outputs into precise, structured responses using advanced prompt engineering including role prompting, JSON-formatted replies, and self-critique loops. Then, you’ll build a robust API layer with caching, rate-limit handling, retries, and token budgeting for stability and cost efficiency. Finally, you’ll design an interface that gathers real user feedback ratings, flags, and clarifications turning every interaction into a learning loop. You’ll work with real tools like OpenAI API, FastAPI, React, Vercel AI SDK, and Postman, completing guided labs and an end-to-end project. This course is for Developers, AI engineers, and UX designers seeking to optimize and integrate Large Language Model (LLM) applications for scalable, reliable, and user-centered solutions. Basic Python or JavaScript skills, familiarity with APIs, and a general understanding of Large Language Model (LLM) concepts and their practical applications. By the end, you’ll have built and optimized your own mini LLM app structured, reliable, and user-centered ready for real-world deployment.

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Starweaver

Global Leaders in Professional & Technology Education

Karlis Zars

Computer Science Ph.D., Trainer and Consultant