LLM Applications: Prototyping, and Evaluation

About this course

Stop "Vibes-Testing" Your AI. Start Engineering for Performance.

Most developers can build an LLM demo in an afternoon, but very few can prove it is ready for production. Large Language Models don't fail loudly with error codes; they fail confidently with hallucinations, incorrect facts, and misleading sources. If you are building Retrieval-Augmented Generation (RAG) systems, you need more than just better prompts—you need a systematic way to measure, trace, and improve your application.

This course, LLM Applications: Prototyping, Evaluation, and Performance, is a comprehensive technical guide designed to take you from a "prompt hacker" to a professional LLM Engineer. We bridge the gap between experimental notebooks and production-grade infrastructure.

What You Will Master

This journey is structured into five core pillars, moving from theory to hands-on cloud deployment:

The Evaluation Mindset: Understand why LLMs fail and why traditional software testing falls short. You’ll learn the risks of ignoring evaluation and how to build a roadmap for systematic quality control.

Deep-Dive RAG Architecture: We deconstruct the RAG pipeline—from the retriever to the generator—identifying the exact failure modes where context gets lost or models hallucinate.

The RAGAS Framework: Master the industry-standard toolkit for automated evaluation. You will learn to quantify Context Precision, Context Recall, and Faithfulness using real-world code walkthroughs and synthetic test data generation.

Full-Stack Observability with LangSmith: Learn to see inside the "black box.

" You will use LangSmith to trace every step of your application’s logic, debug bottlenecks, and turn production data into valuable experiments.

Cloud Operationalization on AWS: Finally, move your workflow into the re

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