Building and Evaluating LLM-Powered Apps on AWS

Building and Evaluating LLM-Powered Apps on AWS free download

Harness the power of Amazon Bedrock to build, evaluate, and deploy intelligent LLM-powered applications with confidence.

This course, "Building and Evaluating LM-Powered Apps on AWS," offers a comprehensive and practical journey into the world of Large Language Models (LLMs) and their application development on the Amazon Web Services (AWS) cloud, with a strong focus on Amazon Bedrock.

You'll begin by gaining a solid understanding of Amazon Bedrock's capabilities as a fully managed service that provides serverless access to a diverse range of high-performing Foundation Models (FMs) from leading AI providers like Anthropic, AI21 Labs, Cohere, and Amazon's own Titan models. We'll demystify why Bedrock is a game-changer for developers, abstracting away the complexities of model hosting and infrastructure management.

The core of the course is intensely hands-on. You'll learn to set up a secure and efficient AWS environment for LLM development, including the configuration of IAM roles and permissions, and mastering the use of the AWS Command Line Interface (CLI) and Boto3 SDK for programmatic interaction with Bedrock. This foundational knowledge will empower you to interact directly with various LLMs, experiment with different model parameters (like temperature and top-p), and utilize the Chat Playground for rapid prototyping and prompt engineering.

A significant portion of the course is dedicated to building sophisticated LLM applications. You'll dive deep into building intelligent agents using AWS Bedrock Agents, learning how to design their workflows, integrate custom tools via AWS Lambda functions to extend their capabilities (e.g., fetching real-time data or interacting with external APIs), and handle complex, multi-step tasks. You'll also master the art of Retrieval-Augmented Generation (RAG), a powerful technique to enhance LLM responses by grounding them with your own proprietary data. This involves practical steps like embedding and indexing documents in a knowledge base, performing vector searches, and augmenting LLM prompts to generate contextually rich and accurate answers.

Crucially, the course doesn't stop at building. You'll learn the vital skill of evaluating your LLM applications. We'll cover various evaluation techniques, including the use of Amazon Bedrock's "LLM-as-a-judge" feature, and methods for running comparisons and scoring outputs. You'll learn to measure key metrics such as response quality, factual correctness (minimizing hallucinations), and relevance to user queries, ensuring your applications are not only functional but also performant and reliable in real-world scenarios.

By the conclusion of this course, you will possess the practical skills and confidence to design, develop, deploy, and rigorously evaluate your own intelligent, production-ready, and cost-aware LLM-powered applications on Amazon Bedrock, whether for chatbots, knowledge assistants, or novel generative AI solutions.