Course Introduction

Generative AI is reshaping how modern applications are built—enabling intelligent, scalable, and highly personalized experiences. Amazon Bedrock is a fully managed AWS service that exposes high-quality foundation models through a simple API, making it easier for developers and organizations to integrate generative AI into production systems. Welcome to the Introduction to Amazon Bedrock course. I’m Alistair, your instructor. In this lesson we’ll cover the essentials of Bedrock: what it is, how it fits into the generative AI landscape, how to access it, and how to use it safely and effectively in real-world systems.

This course assumes basic familiarity with AWS concepts (IAM, S3, Lambda, VPCs). If you are new to AWS, refer to the AWS Documentation before proceeding.

What you’ll learn

This course provides a hands-on, practical introduction to Amazon Bedrock and related generative AI topics:

Core Bedrock concepts and architecture
How to access Bedrock via Console, AWS CLI, and SDKs (for example, the AWS SDK for Python: boto3)
Prompt engineering and model behavior control
Common task patterns: summarization, Q&A, sentiment, and code generation
Integration patterns with AWS services for event-driven systems
Security and governance best practices (IAM, KMS)
Observability, performance, and cost optimization
Retrieval-augmented generation (RAG) and Bedrock Knowledge Bases
Agent-based workflows and how they compare to Model Context Protocol patterns
Hands-on labs and a guided marketing-email example to reinforce learning

Below is the course curriculum overview slide from the lesson:

A split-screen slide: the left side lists an "AWS Bedrock Curriculum" with bullet points, and the right side shows a bearded man speaking while wearing a KodeKloud t-shirt.

Accessing Amazon Bedrock

You can interact with Bedrock in multiple ways:

Console: Quick exploration, model selection, and basic testing.
AWS CLI / API: Scripting, automation, and integration with CI/CD.
SDKs: Programmatic access from applications—commonly using boto3 for Python.

Here’s a simple diagram showing a typical REST inference workflow (HTTP request → server processing → JSON response):

A presentation slide titled "Workflow: REST Inference Example" showing a three-step diagram: Send HTTP request → Server processes request → Return JSON response. Icons illustrate each step and a small presenter thumbnail appears in the lower-right.

Prompt engineering and model interactions

We’ll cover how foundation models interpret instruction-style prompts, how to structure inputs, and techniques to steer generation (temperature, max tokens, system vs. user prompts, few-shot examples). You’ll learn practical prompt patterns and fail-safes to improve reliability. Common task types covered:

Summarization
Question answering
Sentiment analysis
Code generation and transformation

Integrations and event-driven patterns

Integration with other AWS services is a key focus: designing event-driven pipelines that respond to new data and scale with demand. The course demonstrates connecting Bedrock with Amazon S3, AWS Lambda, API Gateway, and Amazon Lex, enabling both backend pipelines and conversational experiences. The next slide shows a sample event-driven pipeline where new S3 objects trigger Bedrock processing:

A presentation slide titled "Results" showing an event-driven AWS workflow where new files uploaded to Amazon S3 trigger processing by Bedrock. A small circular video thumbnail of a person appears in the lower-right corner.

Common integrations at a glance:

AWS Service	Typical role with Bedrock	Example use case
Amazon S3	Storage and event source	Trigger inference on uploaded documents
AWS Lambda	Lightweight processing & orchestration	Pre/post processing, calling Bedrock Runtime
Amazon API Gateway	Public APIs for inference	Expose model endpoints to web or mobile apps
Amazon Lex	Conversational UI	Forward user intents to Bedrock for complex responses

A typical conversational architecture (Lex → Lambda → Bedrock → Lex) is illustrated below:

A presentation slide titled "Workflow: Who Does What?" that diagrams an AWS Bedrock chatbot workflow from User through Amazon Lex, Amazon Lambda, Amazon Bedrock and back, with icons and brief labels. A small circular webcam view of a presenter appears in the bottom-right.

Security, governance, and best practices

Security is essential when building generative AI systems. You will learn:

Fine-grained access control with IAM (least privilege for Bedrock actions)
Encrypting model inputs/outputs and artifacts with AWS KMS
Governance and model usage monitoring
Responsible AI considerations and content filtering patterns

Carefully design IAM policies and KMS usage. Grant only the required Bedrock actions and limit resource ARNs to avoid unintentionally exposing model invocation or knowledge-base access.

Example IAM policy for Bedrock usage (showing model invocation and RAG retrieval permissions):

{
  "Version": "2012-10-17",
  "Statement": [
    {
      "Sid": "AllowModelUsage",
      "Effect": "Allow",
      "Action": [
        "bedrock:InvokeModel"
      ],
      "Resource": [
        "arn:aws:bedrock:us-east-1::foundation-model/anthropic.claude-3-sonnet"
      ]
    },
    {
      "Sid": "AllowKnowledgeBaseRAG",
      "Effect": "Allow",
      "Action": [
        "bedrock:RetrieveAndGenerate"
      ],
      "Resource": [
        "arn:aws:bedrock:us-east-1:123456789012:knowledge-base/kb-abc123"
      ]
    }
  ]
}

Deployment patterns, observability, and cost optimization

We explore options for running Bedrock-driven workloads inside private VPCs, adding CloudWatch metrics/logs for observability, and techniques to analyze performance and reduce inference cost (batching, caching, appropriate model selection, autoscaling).

Agent-based workflows and RAG

The course explains Bedrock Agents—how they orchestrate actions and connect to APIs—and compares agent-based solutions to Model Context Protocol approaches and RAG patterns using Bedrock Knowledge Bases. Here’s a screenshot showing the Agent builder and action group configuration in the Bedrock interface:

Hands-on labs and sample code

This course includes guided labs and real-world demos. Below is a short setup snippet that demonstrates initializing a Bedrock runtime client with boto3 and preparing a simple prompt. This is a setup example—actual inference calls will follow the Bedrock runtime API you use.

import boto3

client = boto3.client("bedrock-runtime", region_name="us-east-1")

model_id = "us.meta.llama3-1-8b-instruct-v1:0"

prompt = """
Instruction:
You are an AWS instructor.

Input:
Explain Amazon VPC by:
"""

In later modules you’ll use the appropriate runtime invocation to send this prompt to your chosen foundation model and handle model outputs.

Course format and community

Lectures with visual diagrams and demos
Guided labs and exercise projects (example: a marketing email generation app combining front-end and Bedrock-backed APIs)
Community support through discussion channels where you can ask questions and share solutions

By course end you will be able to design, build, secure, monitor, and optimize generative AI applications on Amazon Bedrock.

Links and references

If you’re ready, let’s begin your journey into generative AI.

Introduction to Gen AI and LLMs

Introduction to Amazon Bedrock

Getting Started With Amazon Bedrock

Foundation Models in Amazon Bedrock

Basic Concepts of Prompt Engineering

Introduction to Text Generation With Amazon Bedrock

How Foundation Models Process Input

Extract Insights Using Prompt Engineering

Working With Different Task Types

Integrating Amazon Bedrock With Other AWS Services

Build a Marketing Email Generation Application

Introduction to Retrieval Augmentation With Bedrock Knowledge Bases

Best Practices and Optimization

Governance, Safety in Responsible AI

Security

Monitoring and Logging

Taking action with Bedrock Agents

Advanced Topics (Optional)

Final Project

Wrap Up

Course Introduction

What you’ll learn

Accessing Amazon Bedrock

Prompt engineering and model interactions

Integrations and event-driven patterns

Security, governance, and best practices

Deployment patterns, observability, and cost optimization

Agent-based workflows and RAG

Hands-on labs and sample code

Course format and community

Links and references

Watch Video

​What you’ll learn

​Accessing Amazon Bedrock

​Prompt engineering and model interactions

​Integrations and event-driven patterns

​Security, governance, and best practices

​Deployment patterns, observability, and cost optimization

​Agent-based workflows and RAG

​Hands-on labs and sample code

​Course format and community

​Links and references

Watch Video

What you’ll learn

Accessing Amazon Bedrock

Prompt engineering and model interactions

Integrations and event-driven patterns

Security, governance, and best practices

Deployment patterns, observability, and cost optimization

Agent-based workflows and RAG

Hands-on labs and sample code

Course format and community

Links and references