Building Generative AI Applications using Amazon Bedrock
Field | Description / Template |
|---|---|
Purpose | This course enables learners to build, customize, and deploy generative AI applications using Amazon Bedrock. It covers foundation models, prompt engineering, RAG architectures, agents, and governance. Learners will gain hands-on experience building production-ready GenAI systems such as chatbots, document Q&A systems, and AI-powered workflows. |
Audience | Developers, AI/ML engineers, data engineers, cloud engineers, and AWS users interested in building generative AI applications. |
Role | AI Engineer, Machine Learning Engineer, GenAI Developer, Cloud Engineer, Solutions Architect. |
Domain | Generative AI, Machine Learning, Cloud Computing |
Skill Level | Intermediate |
Style | Hands-on, project-based learning with real-world GenAI applications, labs, and architecture-driven explanations. |
Duration | 3 Day |
Related Technologies | Amazon Bedrock, AWS Lambda, Amazon S3, Amazon OpenSearch Serverless, Boto3 (Python SDK), LangChain, Foundation Models (Claude, Llama, Titan), Vector Databases |
Course Description
“Building Generative AI Applications with Amazon Bedrock” is a practical, engineering-focused course designed to help developers and cloud professionals create modern AI applications using AWS’s fully managed Generative AI platform.
The course introduces Amazon Bedrock and its ecosystem of foundation models from providers such as Anthropic, Meta, and AI21 Labs. Students will learn how to invoke models programmatically, tune inference parameters, implement guardrails for responsible AI, and architect AI-powered systems using AWS-native services.
Build conversational agents, vector-based search systems, and fully managed RAG applications using Bedrock Knowledge Bases, OpenSearch Serverless, LangChain, and Bedrock Agents. The course also covers embeddings, memory management, orchestration patterns, and evaluation strategies for measuring AI response quality.
Through hands-on labs and real-world examples, participants will gain practical experience designing secure, scalable, and production-ready Generative AI systems on AWS.
Who is this course for
Developers, backend engineers, and startup teams building AI-powered, cloud-native applications using AWS and LLM APIs
Cloud engineers and AWS professionals transitioning into Generative AI and advanced AI workloads
AI engineers implementing RAG pipelines, agentic systems, and Amazon Bedrock-based solutions
Solutions architects designing scalable, enterprise-grade AI platforms on AWS
Technical professionals exploring Amazon Bedrock capabilities and production-ready GenAI architectures
Course Objectives
By the end of this course, learners will be able to:
Understand Amazon Bedrock architecture, ecosystem components, and foundation model selection for diverse business use cases
Invoke foundation models using AWS SDKs and configure inference parameters like Temperature, Top-P, and Stop Sequences
Implement secure and compliant AI applications using Amazon Bedrock Guardrails
Build end-to-end RAG and conversational AI systems using embeddings, vector stores, LangChain, Bedrock Agents, memory management, and Bedrock Knowledge Bases
Architect, evaluate, and scale production-ready AI systems on AWS using serverless patterns and frameworks like RAGAS
Prerequisites
Basic understanding of cloud computing concepts
Familiarity with Python programming
Beginner-level knowledge of APIs and JSON
Basic understanding of AI/LLM concepts is recommended
Familiarity with AWS fundamentals such as IAM and S3 is helpful
Course outline
Section 1: Introduction to Amazon Bedrock
What is Amazon Bedrock? (The Serverless Advantage).
Exploring the Ecosystem: Providers (Anthropic, Meta, AI21, etc.).
Business Use Cases: From Content Generation to Code Assistance.
Lab 1: Model Access & Text Playground
Lab 2: Image & Multimodal Playground
Section 2: Foundation Models & Governance
Inference Deep Dive: Understanding Temperature, Top-P, and Stop Sequences.
The API Reference: Mastering
InvokeModelvs.InvokeModelWithResponseStream.Governance: Setting up Amazon Bedrock Guardrails to filter PII and toxic content.
Lab 5: Boto3 Model Invocation
Lab 4: Streaming Inference Responses
Lab 6: Bedrock Guardrails with Boto3
Section 3: Customization & Architecture
Customizing FMs: When to use Fine-tuning vs. RAG (Retrieval-Augmented Generation).
Embeddings & Vector Databases: How Titan Text Embeddings turn text into math.
Architecture Patterns: Building the "Proactive" vs. "Reactive" AI application.
Lab 7: Generating Titan Embeddings
Lab 8: Cosine Similarity Search with NumPy
Section 4: Orchestration with LangChain & Agents
LangChain Fundamentals: Chains, Memory, and Prompt Templates.
Amazon Bedrock Agents: Automating multi-step tasks with Action Groups.
Memory Management: How to keep "state" in a stateless API environment.
Lab 9: LangChain Conversation Memory
Lab 10: Bedrock Agents & Lambda Action Groups
Lab 11: Agent Reasoning & Tool Invocation Testing
Section 5: Knowledge Bases & Advanced RAG
The RAG Pipeline: Data Ingestion -> Chunking -> Embedding -> Storage.
Managed RAG: Simplifying the stack with Amazon Bedrock Knowledge Bases.
Advanced Evaluation: Using the RAGAS framework to measure Faithfulness and Relevancy.
Lab 12: Knowledge Base & Vector Store Setup
Lab 13: Document Ingestion & Synchronization
Lab 14: End-to-End RAG App with Streamlit
Lab 15: RAG Evaluation with RAGAS