Mastering LLM App Evaluation: RAGAS, LangSmith, and AWS

Prototyping, Model Evaluation, and Improving Model Performance, using RAGAS, Langsmith, AWS

What you’ll learn
✓ Implement Systematic Evaluation: Move beyond “vibes” by building rigorous frameworks to measure LLM accuracy, groundedness, and overall system performance.
✓ Master RAGAS Framework: Gain hands-on experience using RAGAS to automate metrics like Context Precision, Recall, and Faithfulness for RAG pipelines.
✓ Advanced Tracing with LangSmith: Master full-stack observability by tracing complex chains, debugging failures, and creating datasets from production traces.
✓ Operationalize on AWS: Learn to set up professional development environments and evaluate production-grade RAG applications within the AWS ecosystem.

Requirements
● Required Skills & Experience Intermediate Python Proficiency: You should be comfortable with Python syntax, data structures, and basic asynchronous programming to work with LangChain and RAGAS scripts. Basic LLM Familiarity: A foundational understanding of how Large Language Models work and basic experience with prompt engineering will help you grasp the evaluation concepts faster. Fundamental NLP Concepts: Familiarity with concepts like embeddings, vector databases, and semantic search is recommended but not strictly required. Tools & Equipment Development Environment: A computer with Python 3.9+ installed and a code editor (like VS Code or Jupyter Notebooks). API Access: You will need an OpenAI API Key (or equivalent) to run the LLM-based evaluation metrics and the RAG application. Cloud Access: An AWS Account (Free Tier is sufficient) for the final module on cloud environment setup and deployment. LangSmith Account: A free LangSmith account for tracing and observability exercises. Note for Beginners: If you have never built a RAG application before, don’t worry! While some coding experience is necessary, we provide a full walkthrough of the base application before we start the evaluation phase.

Description
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 real world. We cover setting up an AWS environment for LLM development, ensuring your evaluation strategy is scalable, secure, and cost-effective.

Hands-On Learning

This is not just a theory course. You will work with

• Real Code: Walk through “Chat with Your Data” applications.

• Industry Tools: Get practical experience with RAGAS, LangChain, LangSmith, and AWS.

Why Take This Course?

By the end of this course, you won’t be guessing if your AI works. You will have the data, the traces, and the infrastructure to prove it. Whether you are a beginner looking for the right start or an intermediate developer needing to solve hallucination issues, this course provides the professional framework to ship AI with confidence.

Who this course is for
■ Beginner Developers: Those who have experimented with simple prompts and want to understand the professional engineering standards required to build more than just a “demo”. Intermediate AI Engineers: Practitioners who have built RAG pipelines but are struggling with “hallucinations” or inconsistent outputs and need a metric-driven way to improve their systems. Data Scientists & Analysts: Professionals looking to move from model experimentation to operationalizing LLM applications in a scalable, traceable, and production-ready environment. Cloud & DevOps Engineers: Anyone tasked with setting up, monitoring, and evaluating the performance of LLM infrastructures within an enterprise AWS ecosystem. Curious Builders of All Levels: If you are interested in the “how” and “why” behind AI reliability—moving from “vibes-based” testing to scientific evaluation—this course provides the roadmap you need.