Build production generative AI applications on AWS with Amazon Bedrock APIs, RAG, Knowledge Bases, Guardrails, and Bedrock Agents. 2-day advanced developer course with 8 hands-on labs.

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Course Overview

There is a wide gap between calling a model API and shipping a generative AI application that an enterprise can trust. This advanced course closes it. Over two days, developers learn to build and customize AI solutions by working with Amazon Bedrock programmatically, not through the console.

You will invoke foundation models directly through Bedrock APIs, including streaming patterns, and add conversational memory to extend context across turns. You will implement Retrieval Augmented Generation using Amazon Bedrock Knowledge Bases so models can answer from your own data, and integrate open source frameworks like LangChain where they earn their place. A full module covers evaluation, which is where most generative AI projects quietly fail: assessing model output, measuring RAG quality, and optimizing latency and cost. Responsible AI is handled practically through Amazon Bedrock Guardrails rather than as an abstract principle.

The final third of the course is agentic. You will work with tools, agent frameworks, and interoperability, then build Amazon Bedrock Agents, inline agents, Bedrock Flows, and multi-agent collaboration, including Amazon Bedrock AgentCore. The closing lab brings it together: a Bedrock agent integrated with both Knowledge Bases and Guardrails.

Eight hands-on labs run throughout. This is a build course, not a survey course.

Mode of Training

🏫 Classroom 💻 Live Online 🧪 Blended 👨‍👩‍👧‍👦 Private Group

Upcoming Schedules

Start Date	Time	Duration	Mode	Price
Sep 03, 2026	9:00 am - 5:00 pm	2 Days	online	$1199	GTR
Sep 17, 2026	9:00 am - 5:00 pm	2 Days	online	$1199	GTR
Oct 01, 2026	9:00 am - 5:00 pm	2 Days	online	$1199	GTR
Oct 15, 2026	9:00 am - 5:00 pm	2 Days	online	$1199	GTR
Oct 29, 2026	9:00 am - 5:00 pm	2 Days	online	$1199	GTR

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What you will learn

Develop generative AI applications using Amazon Bedrock
Design architecture patterns for generative AI applications
Configure Amazon Bedrock APIs to invoke foundation models programmatically
Build agentic AI applications by integrating Bedrock tools and open source frameworks
Build custom solutions with Retrieval Augmented Generation and Amazon Bedrock Knowledge Bases
Integrate open source SDKs with Amazon Bedrock for business applications
Optimize model responses through prompt engineering techniques
Evaluate generative AI application components, model output, and RAG quality
Implement responsible AI practices using Amazon Bedrock Guardrails

Who Should Attend This Course?

Software developers building generative AI applications on AWS. The course also fits:

Solution architects designing agentic and RAG-based systems
Technical leads evaluating Amazon Bedrock for production workloads
Machine learning engineers moving into LLM application development
Platform engineers responsible for deploying and governing AI workloads

Prerequisites

AWS recommends attendees have:

Completed Generative AI Essentials on AWS, or equivalent foundational knowledge of generative AI concepts
Intermediate-level proficiency in Python
Familiarity with the AWS Cloud

The Python requirement is real. The labs are code-based, and this course assumes you can read and write Python comfortably rather than follow along by copying.

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📘 Developing Generative AI Applications on AWS Outline

Generative AI concepts in an application context
The AWS generative AI stack and its components
Designing the components of a generative AI application

Guiding model response generation
Using Amazon Bedrock programmatically
Hands-on lab: developing with Amazon Bedrock APIs
Hands-on lab: developing streaming patterns with Amazon Bedrock APIs

Introduction to prompt engineering
Core prompting techniques
Optimizing prompts for better results

Implementing architecture patterns with Bedrock APIs
Common use cases in practice
Adding conversational memory to extend context
Hands-on lab: developing conversation patterns with Amazon Bedrock APIs

Implementing Retrieval Augmented Generation
Working with Amazon Bedrock Knowledge Bases
Hands-on lab: developing RAG applications with Amazon Bedrock Knowledge Bases

Invoking a Bedrock foundation model using LangChain
Using LangChain for context-aware responses
Hands-on lab: developing a generative AI application pattern using open source frameworks and Bedrock Knowledge Bases

Evaluating application components
Evaluating model output
Evaluating RAG output
Optimizing latency and cost
Hands-on lab: evaluating Retrieval Augmented Generation applications

Understanding responsible AI in practice
Mitigating bias and addressing prompt misuse
Using Amazon Bedrock Guardrails
Hands-on lab: securing generative AI applications using Bedrock Guardrails

Working with tools
Understanding AI agents
Open source agentic frameworks
Agent interoperability

Implementing Amazon Bedrock Flows
Designing Amazon Bedrock Agents
Developing Bedrock inline agents
Designing multi-agent collaboration
Using Amazon Bedrock AgentCore
Hands-on lab: developing Amazon Bedrock Agents integrated with Knowledge Bases and Guardrails

❓ Frequently Asked Questions

Substantially. Eight labs run across the two days, covering Bedrock APIs, streaming, conversation patterns, RAG with Knowledge Bases, open source framework integration, RAG evaluation, Guardrails, and agents. Nearly every module ends with you building something.

AWS recommends it, and it's sensible if generative AI concepts are new to you. If you already understand foundation models, prompting, and the general landscape, you can come straight into this course, provided your Python is solid.

AgentCore is part of the agentic tooling covered in the final module, alongside Bedrock Flows, inline agents, and multi-agent collaboration. This section is the biggest reason to take the current version of the course: agentic architectures have moved from experimental to expected, and this is where the practical implementation is taught.

Essentials is one day, fundamental level, and built for business audiences with no coding. This is two days, advanced, and entirely code-based. Essentials teaches you to identify use cases and prompt well; this teaches you to build, evaluate, secure, and ship the application. Many organizations send both audiences, to their respective courses.

Yes, in Module 6, framed as open source framework integration rather than the centrepiece it once was. You will invoke Bedrock models through LangChain and use it for context-aware responses, then build a full application pattern combining it with Knowledge Bases.

Not directly. It's a technical skills course rather than exam preparation, though the Bedrock, RAG, and responsible AI content is strong background for AWS AI and machine learning certification paths.

Still have questions?

Reach out to our learning advisors for personalized guidance on choosing the right course, group training, or enterprise packages.

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