Data Structures and Schemas in AWS GenAI

Structured data plays a foundational role in the reliability and predictability of generative AI systems built on AWS. In most production environments, unexpected model behavior is less often caused by the foundation model itself and more commonly tied to how input data is prepared and structured before inference. Foundational models infer meaning from organized, labeled, and ordered data rather than just raw content.

This lesson examines structured data as a core design concern in GenAI systems and explains how format engineering and preprocessing directly influence model behavior and output consistency. We’ll cover the following topics in this lesson:

• Structured data in GenAI systems: Why foundation models are sensitive to schema consistency, and how structured data flows through ingestion, RAG, and prompt construction pipelines, including how it is enforced at inference time through structured request payloads.

• Format engineering for foundation model inputs: Designing consistent schemas, normalizing fields, and aligning structured inputs with prompt expectations to improve model interpretation and output stability.

• Structured data formatting for AWS GenAI services: Preparing service-specific structured inputs for Amazon Bedrock, Amazon SageMaker AI endpoints, and dialog-based applications, including request schemas and conversation state.

• On-the-fly data cleansing and preprocessing: Using AWS Lambda for real-time validation, normalization, and PII handling to ensure data quality and compliance before inference.

• Handling multimodal and complex structured data: Organizing structured metadata alongside text, image, audio, and tabular inputs to support ...