AWS Certified Generative AI Developer Professional (AIP-C01) Free Learning Path

Lesson 1: Day - 1 | AWS Certified Generative AI Developer Professional (AIP-C01) - Full Course Roadmap 2026

This opening episode lays out the roadmap for the AWS Certified Generative AI Developer Professional (AIP-C01) course. It frames the series as a path from prompt engineering toward generative AI architecture and previews the exam domains covered later. It is a short orientation video rather than a technical lesson.

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Lesson 2: Day - 2 | Amazon Bedrock Tutorial for Beginners - AWS GenAI Professional Course (Ep. 2)

An introduction to Amazon Bedrock as a fully managed service for accessing foundation models without managing GPU servers. The episode explains where Bedrock fits in the AWS AI stack and why it matters for the AIP-C01 exam. It sets up the hands-on work in later episodes.

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Lesson 3: Day - 3 | Episode-3: STOP! Do This Before Using Amazon Bedrock (AWS Billing Alerts)

A short but important warning that Amazon Bedrock is not free and charges per token. The episode walks through setting up AWS billing alerts before running any models. It is a practical step to avoid surprise charges while following the course.

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Lesson 4: Day - 4 | Episode-4: AWS GenAI Security: Setting Up IAM Users & Permissions (AIP-C01)

This episode covers securing an AWS environment by moving away from the root account. It shows how to create IAM users and assign permissions for day-to-day development. The focus is on basic account hygiene before building generative AI workloads.

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Lesson 5: Day - 5 | AWS Bedrock Fine-Tuning Tutorial: Step-by-Step Guide for Developers

A step-by-step guide to fine-tuning models in Amazon Bedrock. The episode explains when fine-tuning makes sense compared with RAG, and covers related techniques such as LoRA and continued pre-training. It is one of the longer technical lessons in the early part of the course.

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Lesson 6: Why Every AI Engineer Needs to Know RAG

This lesson introduces Retrieval-Augmented Generation as a way to connect an LLM to private company data without retraining it. It explains the amnesia problem with base models and how retrieval solves it. The episode is conceptual and prepares viewers for the hands-on RAG build that follows.

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Lesson 7: Build a Custom AI on AWS Bedrock: Hands-On RAG Pipeline Demo (GenAI Ep 9)

A long hands-on walkthrough of building a RAG pipeline in the AWS Console. It moves from architecture diagrams to an actual implementation using Bedrock and related services. This is one of the most practical episodes in the RAG portion of the course.

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Lesson 8: Day-8 Avoid the ~$300 AWS Bill! Complete RAG Pipeline Cleanup.

A cleanup episode that shows how to tear down the RAG pipeline resources created in the previous demo. It focuses on avoiding a large AWS bill from forgotten services such as OpenSearch. The lesson is short but important for anyone following along in their own account.

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Lesson 9: The Chunking Mistake Every RAG Developer Makes

This episode explains why chunking strategy often causes poor RAG answers. It covers how documents are split before embedding and how that affects retrieval quality. The lesson argues that even a strong LLM cannot compensate for badly chunked source data.

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Lesson 10: Amazon's S3 Vectors: Serverless Storage That Saves Thousands

An overview of Amazon S3 Vectors as a lower-cost alternative to OpenSearch for vector storage in Bedrock workloads. It explains vector buckets and indexes and positions S3 Vectors for cost-sensitive use cases. The episode is mostly architectural rather than hands-on.

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Lesson 11: Build a 90% Cheaper Vector DB: S3 Vector Store Demo (AWS)

A hands-on demo that builds a vector store using S3 Vectors in the AWS Management Console. It follows up on the earlier architecture discussion and shows how to set up a large-scale RAG pipeline at lower cost. The focus is on practical configuration steps.

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Lesson 12: AWS Vector Databases Explained | Which One Should You Use?

A comparison of AWS vector database options and guidance on when to use each one. It covers selection criteria for RAG, semantic search, and recommendation workloads. The episode helps viewers choose between services rather than committing to one by default.

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Lesson 13: AWS Bedrock Guardrails Explained in 6 Minutes | Control AI Outputs

A short beginner-friendly explanation of AWS Bedrock Guardrails. It covers what guardrails are, why they matter, and how they help prevent harmful or non-compliant AI responses. The episode is conceptual and pairs with the hands-on lab that follows.

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Lesson 14: Mastering AWS Bedrock Guardrails | Complete Hands-On Lab

A hands-on lab for configuring and testing Bedrock Guardrails in the AWS Console. It walks through the major guardrail features and shows how to apply them in a real environment. This episode assumes you already understand the guardrails concepts from the previous lesson.

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Lesson 15: The Prompt Management Game-Changer in AWS Bedrock

This lesson covers AWS Bedrock Prompt Management for creating, testing, versioning, and managing prompts at scale. It uses practical examples to show how to avoid hardcoding prompts in application code. The goal is production-ready prompt workflows.

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Lesson 16: Multimodal Data Automation: How AWS Bedrock Transforms Unstructured Content

A short introduction to Amazon Bedrock Data Automation and how it turns unstructured content into structured business data. It covers documents, images, videos, and audio as inputs. The episode sets up the hands-on BDA tutorial that follows.

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Lesson 17: AWS Bedrock Data Automation for Beginners | Hands-On Tutorial

A hands-on lab that builds a complete Bedrock Data Automation workflow from scratch in the AWS Console. It shows how to process unstructured content and generate structured outputs for AI applications. This is the practical follow-up to the BDA overview.

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Lesson 18: Agentic AI vs Traditional LLMs | What's Actually Different?

This episode explains what makes agentic AI different from a traditional LLM call. It walks through reasoning, tool selection, API calls, and response generation using AWS concepts. The lesson is conceptual and prepares viewers for multi-agent topics.

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Lesson 19: AWS Multi-Agent Systems Explained | Build Smarter AI Agents

An introduction to multi-agent systems on AWS and how multiple agents can collaborate and delegate tasks. It covers orchestration patterns and how these systems are built with Amazon Bedrock. The episode focuses on architecture rather than a full build.

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Lesson 20: AWS Agent Squad Explained | Build Collaborative AI Agents

A short lesson on AWS Agent Squad and how it supports collaborative AI agents. It follows the multi-agent systems episode and introduces Agent Squad as a specific AWS capability. The video is brief and mostly explanatory.

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Lesson 21: AWS Bedrock AgentCore Explained | Build Production-Ready AI Agents

This episode explains AWS Bedrock AgentCore and its role in building, deploying, securing, and scaling production AI agents. It covers the AgentCore architecture and how it moves agents from prototype to production. The focus is on operational concerns rather than a full demo.

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Lesson 22: Build Your First AI Agent on AWS with Amazon Bedrock | Complete Tutorial

A complete end-to-end demo of building an AI agent with Amazon Bedrock. The agent interprets a user request, reasons about the task, invokes an AWS Lambda function, retrieves data from a mock database, and returns a response. This is the first full agent build in the course.

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Lesson 23: I Built a Real-World Customer Support AI Agent with AWS Bedrock

A longer hands-on build of a customer support AI agent using Amazon Bedrock. The agent handles customer requests and interacts with multiple tools to produce useful answers. It goes beyond a basic chatbot demo and shows a more realistic use case.

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