Foundation Models for Text Summarization and Legal AI on AWS

Large language models are transforming how we process text, but applying them to specialized domains like legal documents and long-form articles requires specific strategies. This text-based course guides you through utilizing foundation models on AWS to automate summarization and analyze complex legal texts efficiently. You will transition from understanding basic generative AI concepts to building cost-effective, practical workflows that extract key insights from dense documents. What you'll learn: - Understand the core architecture and capabilities of modern foundation models. - Configure AWS AI services to deploy and query large language models securely. - Apply advanced prompt engineering techniques for adaptive text summarization. - Design cost-effective strategies for fine-tuning and adapting models to legal terminology. - Implement basic retrieval-augmented generation patterns for legal document analysis. We begin with foundational definitions of generative AI and cloud-based models, then progress through step-by-step written explanations and structured code examples for text summarization, fine-tuning strategies, and document processing. This course is designed for beginners, developers, and legal tech enthusiasts who want to apply AI to real-world text challenges, with no prior machine learning experience required. Start reading today to unlock the potential of foundation models for text automation.