Join us as Faye Ellis breaks down what responsible AI actually looks like in practice on Amazon Bedrock - live demos included, straight from her prep for the AWS Generative AI Developer Professional certification.
Faye walks through the NIST AI risk framework's core characteristics of trustworthy systems, then demos two hands-on builds: a RAG pipeline using S3 Vectors as a low-cost knowledge base, and Bedrock Guardrails blocking financial advice, prompt injection attacks, and sensitive information in real time. You'll learn the difference between prompt engineering and guardrails, how contextual grounding checks catch hallucinations before they reach a user, why data cleaning and deduplication matter as much for cost and sustainability as for bias, and what Faye wishes she'd known going into one of AWS's hardest professional-level exams.
Timestamps
0:00 Welcome & Introduction
9:28 What Makes an AI System Trustworthy - The NIST Framework
15:19 Famous AI Failures - From ChatGPT Code Leaks to Biased Systems
22:56 Building RAG with Bedrock Knowledge Bases and S3 Vectors
26:29 Live Demo - Setting Up RAG with S3 Vectors
31:19 Prompt Engineering and Bedrock Prompt Management
34:46 What Are Bedrock Guardrails and How They Work
41:22 Live Demo - Blocking Financial Advice, Jailbreaks, and PII
53:00 Preparing for the AWS Generative AI Developer Professional Exam
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