Introduction: From Theory to Practice—Why Case Studies Matter

Reading about riding a bike is helpful, but you only truly learn by getting on and pedaling. The same goes for generative AI. Understanding the theory is important, but real mastery comes from building and seeing solutions in action. This chapter is your chance to roll up your sleeves and apply what you’ve learned—hands-on—with Bedrock, Textract, Retrieval-Augmented Generation (RAG), and the AWS AI suite.

Bridging the Gap: From Concepts to Practical Solutions

Up to now, you’ve explored the core ideas behind Amazon Bedrock and AWS AI services. You know how foundation models process text and images, how Textract extracts data, and how RAG grounds answers in enterprise knowledge. But theory only gets you so far. The real test is assembling these building blocks to solve actual business problems.

Case studies and mini-projects are your bridge from learning to doing. They show how concepts like prompt engineering (crafting effective instructions for models), agent orchestration (coordinating multi-step workflows), and embedding-based retrieval (using vector search to find relevant information) combine to power real solutions—a voice-enabled support bot, a FinOps cost analyzer, or a multimodal field inspection assistant. By dissecting these examples, you’ll learn not just what and why, but—crucially—how.

Today’s Bedrock solutions also incorporate modern features such as prompt caching and optimization for lower cost and latency, agent APIs for function-calling, and robust guardrails for responsible AI. You’ll see how these are woven into practical blueprints throughout this book.

Why Examples Accelerate Mastery

Seeing theory applied to real scenarios accelerates your learning. For example, it’s one thing to know Bedrock can generate text; it’s another to watch a well-crafted prompt answer a real customer’s question, or see a document pipeline turn invoices into insights. Concrete examples help you:

• Spot best practices and common pitfalls (so you avoid costly mistakes)
• Learn how to connect AWS services, from authentication to error handling
• See the business impact—cost, compliance, and user experience
• Understand how to use prompt caching, guardrails, and logging for enterprise-readiness

Let’s look at a foundational pattern you’ll use throughout the case studies: invoking a Bedrock model to answer a user’s question.

Invoking a Bedrock Model for Q&A (2025 Best Practices)

# Import Bedrock client from boto3import boto3
import json
import logging
# Set up structured logging for observability (recommended for production)logging.basicConfig(level=logging.INFO, format='%(asctime)s %(levelname)s %(message)s')
logger = logging.getLogger(__name__)
# Initialize the Bedrock client (ensure your AWS credentials are set)bedrock_client = boto3.client('bedrock-runtime')
# Define a prompt for the model based on a real user questioncustomer_question = "How do I reset my account password?"prompt = f"""You are a helpful customer support assistant. A customer asked:"{customer_question}"Provide a clear, concise answer and suggest next steps if needed."""# Prepare the request body as JSONpayload = {
    "prompt": prompt,
    "max_tokens": 200}
# For production, consider enabling prompt caching and optimization in Bedrock# to reduce costs and improve latency (see Chapter 5).# For enterprise deployments, integrate Bedrock Guardrails to mitigate prompt injection# and ensure responsible AI outputs (see Chapter 12).try:
    # Call the latest Bedrock model (e.g., Anthropic Claude 3 Sonnet)    response = bedrock_client.invoke_model(
        modelId="anthropic.claude-3-sonnet-2025",  # Use latest model; see Bedrock docs for updates        contentType="application/json",
        body=json.dumps(payload)
        # Optionally, use model version aliases for easier upgrades/rollbacks    )
    # Parse and print the model's response    result = json.loads(response['body'].read())
    logger.info(json.dumps({"bedrock_response": result}, indent=2))
    print(result.get('completion'))
except Exception as e:
    logger.error(f"Bedrock invocation failed: {e}")
    # Implement error handling and logging as part of production best practices

How this works:

• The payload is a JSON object with your prompt and a token limit.
• The response contains a completion field with the model’s answer.
• The example uses the latest modelId (e.g., anthropic.claude-3-sonnet-2025). Update this as new models become available, or use model version aliases for stability.
• Structured logging is included for easier monitoring and troubleshooting.

Try it yourself: Modify the prompt or question above and see how the output changes. For advanced scenarios, experiment with prompt caching, guardrails, and output logging to gain practical experience with production-grade patterns.

Blueprints: Solution Templates for Real Business Needs

Each case study acts as a blueprint—a reusable template you can adapt for your own projects. Whether you’re modernizing customer support, optimizing costs, or automating inspections, these blueprints:

• Break complex solutions into clear, modular pieces