Course Description: Generative AI on AWS with Large Language Models

This course provides a comprehensive, hands-on introduction to building, integrating, and operationalizing generative AI solutions using AWS technologies. Participants will learn how Large Language Models (LLMs) work, how to leverage AWS Bedrock and related AI services, and how to implement secure, scalable, and cost-efficient generative AI applications tailored for IT operations and enterprise environments.

Through a combination of conceptual instruction, architectural walkthroughs, and practical labs, learners will gain the skills needed to evaluate generative AI opportunities, build working prototypes, integrate models into existing systems, and plan real-world implementation projects.

1. What is generative AI, and how is it different from traditional AI?

Generative AI creates new content—such as text, images, or code—based on patterns learned during training. Traditional AI typically focuses on prediction, classification, or detection. Generative models like LLMs can produce human-like outputs and support advanced reasoning tasks.

2. What is an LLM (Large Language Model)?

An LLM is a deep learning model trained on massive amounts of text data to understand and generate natural language. Examples include Anthropic Claude and Meta Llama, which are accessible through AWS Bedrock.

3. What is AWS Bedrock?

AWS Bedrock is a fully managed service that provides access to leading foundation models through a simple API. It allows you to use generative AI without managing infrastructure or training large models yourself.

4. Do I need to know machine learning to use AWS Bedrock?

No. Bedrock provides ready-to-use models that you can integrate through prompts and APIs. Basic AWS and programming experience is helpful but deep ML expertise is not required.

5. What types of models are available on AWS Bedrock?

AWS Bedrock offers multiple families of foundation models, including:

• Anthropic Claude (text reasoning, conversation, analysis)

• Meta Llama (general-purpose and code generation)

• Other models for text, image generation, embedding, and more.

6. What is prompt engineering, and why is it important?

Prompt engineering is the practice of structuring inputs to guide an LLM’s output. Good prompts improve accuracy, reduce hallucinations, and optimize performance, especially for enterprise use cases.

7. Can I build custom LLM applications on AWS?

Yes. You can integrate Bedrock with Lambda, API Gateway, S3, OpenSearch, and other AWS services to build chatbots, document analysis tools, automation systems, and RAG applications.

8. What is RAG (Retrieval-Augmented Generation)?

RAG combines LLMs with external data retrieval. It allows an LLM to reference your company’s documents or databases, enabling accurate and context-aware responses using up-to-date information.

9. How does AWS ensure security for generative AI applications?

AWS provides IAM-based access control, private networking options, encryption, audit logging, and secure API endpoints. You retain full control over your data and permissions when using Bedrock.

10. How do costs work when using generative AI on AWS?

Costs are typically based on model usage—measured in tokens processed—and the specific model chosen. Optimizing prompts, selecting efficient models, and using caching strategies can help reduce costs.

11. Do I need to train my own model to use generative AI on AWS?

No. AWS Bedrock provides pre-trained foundation models. If needed, Amazon SageMaker can be used for fine-tuning or training custom models, but it's optional.

12. What programming languages can I use to integrate Bedrock?

You can use any AWS-supported SDK, including Python, JavaScript/TypeScript, Java, Go, and others, to call Bedrock APIs and build applications.