Build enterprise-grade generative AI on AWS with RAG, Bedrock Agents, Guardrails, prompt governance, and cost optimization. 3-day advanced course aligned to Exam AIP-C01.

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Course Overview

Most organizations can build a generative AI prototype. Far fewer can run one in production, where reliability, cost, security, and auditability all matter and nobody accepts "the model made something up" as an answer. This three-day course is about closing that gap.

The curriculum follows AWS's own model for generative AI adoption, moving from experimentation to production-ready implementation, and it covers the full stack. You will build an enterprise framework for evaluating and selecting foundation models, then design resilient systems around them with circuit breakers, cross-region deployment, and graceful degradation. You will construct multi-modal data processing pipelines with real validation, implement vector database architectures using Amazon Bedrock Knowledge Bases and OpenSearch, and develop retrieval systems sophisticated enough to hold up against production traffic.

From there the course moves through advanced prompt engineering and the governance frameworks large organizations need to manage prompts centrally, agentic AI with Amazon Bedrock Agents and tool integration, and AI safety covering content filtering, privacy-preserving architecture, and compliance. The final third addresses what teams usually discover too late: token efficiency and cost management, intelligent caching, monitoring and observability for foundation model applications, systematic testing and RAG evaluation, and the enterprise integration patterns needed for secure identity, hybrid deployments, and cross-environment connectivity.

Hands-on labs run throughout, including building RAG applications with Bedrock Knowledge Bases, developing conversation patterns with Bedrock APIs, and securing applications with Guardrails.

Mode of Training

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

Upcoming Schedules

Start Date	Time	Duration	Mode	Price
Sep 02, 2026	9:00 am - 5:00 pm	3 Days	online	$1499	GTR
Sep 16, 2026	9:00 am - 5:00 pm	3 Days	online	$1499	GTR
Sep 30, 2026	9:00 am - 5:00 pm	3 Days	online	$1499	GTR
Oct 14, 2026	9:00 am - 5:00 pm	3 Days	online	$1499	GTR
Oct 28, 2026	9:00 am - 5:00 pm	3 Days	online	$1499	GTR

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What you will learn

Develop production-ready generative AI solutions that meet enterprise requirements for security, scalability, and reliability
Evaluate and select foundation models for specific use cases, including benchmarking and dynamic model selection architectures
Design resilient foundation model systems using circuit breakers, cross-region deployment, and graceful degradation
Build data processing pipelines for multi-modal inputs, with validation workflows and optimization
Implement vector database solutions using Amazon Bedrock Knowledge Bases, OpenSearch, and hybrid approaches
Create advanced prompt engineering frameworks, including chain-of-thought reasoning and enterprise prompt governance
Develop autonomous AI agents with Amazon Bedrock Agents, complex reasoning patterns, and tool integration
Implement AI safety and security controls covering content filtering, privacy preservation, and adversarial testing
Optimize performance and cost through token efficiency, batching, and intelligent caching
Design monitoring and observability solutions for foundation model applications
Build systematic testing and validation frameworks for continuous quality assurance
Integrate generative AI into enterprise environments using secure, compliant, scalable patterns

Who Should Attend This Course?

Software developers and senior developers building generative AI applications on AWS
Machine learning engineers moving into production generative AI work
Cloud engineers and solutions architects designing enterprise AI systems
Engineering managers responsible for delivering generative AI to production

Prerequisites

AWS recommends attendees have:

AWS Technical Essentials, or equivalent practical AWS experience
Generative AI Essentials on AWS, or prior hands-on generative AI experience
Two or more years building production-grade applications on AWS or with open source technologies, plus general AI/ML or data engineering background
One year of hands-on experience implementing generative AI solutions

These prerequisites are meaningful rather than nominal. This is the most advanced course in the AWS generative AI track, and it assumes you have already built generative AI applications and now need to make them production-worthy. If you are earlier in that journey, Developing Generative AI Applications on AWS is the better starting point.

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Certification

This course aligns to the AWS Certified Generative AI Developer – Professional exam (AIP-C01), a professional-level certification for developers who integrate foundation models into applications and business workflows and deploy generative AI into production on AWS. The exam validates the ability to design and implement solutions using vector stores, Retrieval Augmented Generation, knowledge bases, and other generative AI architectures.

Exam scoring is scaled from 100 to 1,000, with a minimum passing score of 750. The exam includes ten unscored questions used by AWS to evaluate future items; these are not identified during the exam.

The course maps closely to the certification's content domains, which span foundation model integration and data management, implementation and integration, AI safety and security, operational efficiency and optimization, and testing and validation.

📘 Advanced Generative AI Development on AWS Outline

Enterprise foundation model evaluation framework
Dynamic model selection architecture patterns
Resilient foundation model system designs
Cost optimization and economic modeling

Data validation and quality assurance
Multi-modal data processing pipelines
Input optimization and performance enhancement

Enterprise vector database architecture
Advanced document processing and chunking strategies
Sophisticated retrieval system implementation
Hands-on lab: developing RAG applications with Amazon Bedrock Knowledge Bases

Advanced prompt engineering frameworks
Complex prompt orchestration systems
Enterprise prompt governance and management
Hands-on lab: developing conversation patterns with Amazon Bedrock APIs

Agentic AI architecture and evolution
Amazon Bedrock Agents implementation
The AWS agentic AI service ecosystem
Tool integration and production observability

Content safety implementation
Privacy-preserving AI architecture
AI governance and compliance frameworks

Token efficiency and cost optimization
High-performance system architecture
Intelligent caching systems
Hands-on lab: building secure and responsible generative AI with Guardrails for Amazon Bedrock

Foundation model monitoring systems
Business impact and value management
AI-specific troubleshooting and diagnostics

Comprehensive AI evaluation frameworks
Quality assurance and continuous improvement
RAG system evaluation and optimization

Enterprise connectivity and integration architecture
Secure access and identity management
Cross-environment and hybrid deployments

Course summary
Next steps and additional resources

❓ Frequently Asked Questions

No, and the difference matters. That course is two days and teaches you to build generative AI applications with Amazon Bedrock, RAG, and agents. This course is three days and assumes you can already do that. Its subject is everything required to run those applications at enterprise scale: model selection frameworks, resilience patterns, prompt governance, cost and token management, observability, evaluation, and enterprise integration. Most people take them in that order.

Amazon Bedrock is central, including Knowledge Bases, Agents, and Guardrails. The course also works with Amazon OpenSearch for vector search, Amazon SageMaker for model customization and MLOps, Amazon Q Developer for development support, and touches on specialized infrastructure such as AWS Trainium and Inferentia for training and inference cost optimization.

Intermediate to advanced. The labs are code-based and assume comfort with Python and generative AI frameworks. This is not a course where you can follow along by copying, and the prerequisites explicitly call for a year of hands-on generative AI implementation experience.

It covers the certification's subject matter closely, but treat it as a major component of preparation rather than the whole of it. AIP-C01 is a professional-level exam, and AWS professional certifications reward substantial hands-on production experience alongside formal training. Plan on additional practice and self-study before booking.

The patterns are deliberately industry-agnostic and built for regulated, large-scale environments. Teams from financial services, healthcare and life sciences, retail, manufacturing, and technology all send people to this course, since the challenges of governance, privacy, cost control, and auditability are shared across sectors.

Yes. Private delivery works well for this course in particular, since the enterprise integration, governance, and compliance content can be discussed against your own architecture and requirements.

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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