Multimodal Generation and Guardrails

This unit teaches hands-on techniques for generating and controlling multimodal outputs with Amazon Bedrock, including image creation with Stable Diffusion and contextual enhancement using multimodal foundational models. Students will learn to tune parameters, implement guardrails, build an end-to-end text-to-SQL pipeline, maintain conversational context in chatbots, and integrate SageMaker JumpStart to prepare models for safe, reliable deployment.

Learning Objectives

• Apply prompt engineering and parameter tuning in Amazon Bedrock to generate and iteratively refine images using Stable Diffusion and multimodal models to meet specified visual and contextual criteria.
• Demonstrate designing and implementing guardrails in Amazon Bedrock—such as content filtering, schema/response validation, and throttling—by building and testing a secured pipeline that detects and mitigates unsafe or out-of-scope outputs.
• Develop an end-to-end text-to-SQL system using Amazon Bedrock that performs natural-language intent parsing, generates syntactically valid SQL, executes queries on a sample dataset, and validates results for correctness.
• Evaluate techniques for maintaining conversational context in generative AI chatbots and integrate Amazon SageMaker JumpStart models to extend functionality, comparing trade-offs in latency, cost, accuracy, and maintainability.