Curriculum Overview: Mastering Amazon Bedrock and Amazon Q

This curriculum provides a comprehensive roadmap for understanding and implementing AWS's primary generative AI platforms. Learners will move from foundational model access via Amazon Bedrock to specialized AI assistance with Amazon Q.

Prerequisites

Before starting this curriculum, students should possess:

• Cloud Fundamentals: Basic knowledge of AWS infrastructure (IAM, VPCs, and S3).
• AI Literacy: Understanding of basic GenAI terms (Tokens, LLMs, and Hallucinations).
• Security Basics: Familiarity with the AWS Shared Responsibility Model.

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Module Breakdown

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Module Objectives per Module

Module 1: Amazon Bedrock Foundations

• Model Access: Explain how to access multiple Foundation Models (FMs) through a single API.
• Parameter Tuning: Master the effects of $Temperature$ and $Top P$ on model creativity.
• Playgrounds: Utilize text, image, and video playgrounds for rapid prototyping.

Module 2: Building with Bedrock

• Retrieval-Augmented Generation (RAG): Implement Knowledge Bases for proprietary data.
• Agentic AI: Configure Amazon Bedrock Agents to execute multi-step business tasks.
• Model Evaluation: Use ROUGE and BLEU scores to assess model performance.

Module 3: Amazon Q Business

• Unified Search: Index corporate data across Slack, Microsoft 365, and SharePoint.
• Q Apps: Create no-code applications for content generation and workflow automation.
• Transparency: Utilize citations and references to ensure response accuracy.

Module 4: Amazon Q Developer

• Development Speed: Increase coding velocity by up to 80% using IDE plugins.
• Modernization: Use agents for heavy-duty tasks like Java or .NET migrations.
• AWS Integration: Query AWS account resources and billing directly via the console.

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Success Metrics

How to know you have mastered the curriculum:

1. Deployment Proficiency: Successfully deploy an Amazon Bedrock Agent that triggers a Lambda function.
2. Productivity Gains: Demonstrate a measurable reduction in coding time using Amazon Q Developer.
3. Accuracy Verification: Maintain a hallucination rate below a defined threshold using Negative Prompts and Guardrails.
4. Financial Efficiency: Optimize model selection based on token pricing and performance needs.

[!IMPORTANT] Success is not just building a model; it is building a safe model. Ensure all applications utilize Amazon Bedrock Guardrails to prevent prompt injection and data leakage.

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Real-World Application

Why this curriculum matters in a career:

• Legacy Modernization: Amazon used Q Developer to migrate tens of thousands of applications to Java 17, saving $260 million and 4,500 years of manual labor.
• Employee Efficiency: Amazon Q Business acts as a 24/7 subject matter expert, reducing the time employees spend searching for internal documentation.
• Lower Entry Barrier: Platforms like PartyRock allow non-developers to create AI tools, democratizing innovation across business units.

Comparison: Bedrock vs. Q

▶Click to expand: Specific Business Use Cases

• Automated Inventory: Using Bedrock to monitor supply chain data and recommend reorder points.
• Code Debugging: Using Q Developer to identify security vulnerabilities in Python or Java code.
• Content Creation: Using Q Apps to generate marketing emails based on internal product manuals.