30-Day Study Plan to Master Generative AI on AWS

Generative AI is no longer just an emerging technology. It is becoming a core business capability across software development, customer support, analytics, content generation, automation, knowledge management, and enterprise productivity. For cloud professionals, developers, data teams, and solution architects, learning Generative AI on AWS is now a high-value career move.
AWS provides a growing ecosystem for building generative AI applications, especially through Amazon Bedrock, which is a fully managed service that helps users access and work with foundation models from Amazon and third-party providers. AWS describes Amazon Bedrock as a service that makes it easier to use foundation models without managing the underlying infrastructure.
This 30-day study plan is designed to help you move from basic generative AI understanding to practical AWS-based implementation. The goal is not only to learn theory, but to build confidence with Amazon Bedrock, prompt engineering, RAG, agents, knowledge bases, responsible AI, security, and cost optimization.
Who Should Follow This 30-Day AWS Generative AI Study Plan?
This learning path is suitable for:
• Cloud engineers
• AWS beginners with basic cloud knowledge
• DevOps engineers
• Solution architects
• Data engineers
• Software developers
• AI/ML beginners
• Project managers working with AI teams
• Technical leads planning GenAI solutions
• Candidates preparing for AWS AI-related certifications
You do not need to be a machine learning expert to start. However, basic knowledge of AWS services, cloud computing, APIs, storage, IAM, and application architecture will help you learn faster.
What You Will Learn in 30 Days
By the end of this study plan, you should understand:
• What generative AI is
• How foundation models work
• How Amazon Bedrock supports GenAI application development
• How to write better prompts
• How to use models for text generation, summarization, Q&A, and chat
• How retrieval-augmented generation works
• How Amazon Bedrock Knowledge Bases help connect models to private data
• How agents can automate tasks
• How to design secure and responsible AI solutions
• How to estimate and optimize GenAI costs
• How to build a basic GenAI project on AWS
AWS Skill Builder also provides dedicated generative AI learning plans for developers who want to use large language models without necessarily fine-tuning them, making it a strong resource to combine with this 30-day plan.
Week 1: Build the Generative AI Foundation
The first week is about understanding the core concepts. Do not rush into tools immediately. A strong foundation will help you understand why services like Amazon Bedrock, SageMaker, Knowledge Bases, and agents matter.
Day 1: Understand Generative AI Basics
Start with the basics of generative AI.
Learn:
• What generative AI is
• How it differs from traditional AI
• How it differs from machine learning
• What foundation models are
• What large language models are
• What text, image, code, audio, and video generation mean
• Common business use cases of generative AI
AWS explains generative AI as a type of AI that can create new content and ideas, including conversations, stories, images, videos, and music.
Practical task:
Write down 10 real-world GenAI use cases, such as chatbot, email generation, code assistant, document summarizer, knowledge assistant, report generator, sales assistant, HR assistant, legal document analyzer, and customer support bot.
Day 2: Learn AI, ML, Deep Learning, and GenAI Differences
Many beginners confuse AI, ML, deep learning, and generative AI. Spend one day clearing this.
Learn:
• Artificial Intelligence
• Machine Learning
• Deep Learning
• Neural networks
• Natural language processing
• Computer vision
• Generative AI
• Foundation models
• Large language models
Example understanding:
AI is the broader field.
Machine learning is a subset of AI.
Deep learning is a subset of machine learning.
Generative AI uses advanced models to generate new content.
Practical task:
Create a simple table comparing AI, ML, deep learning, and generative AI with examples.
Day 3: Learn Foundation Models and LLMs
Foundation models are the backbone of modern GenAI applications. You should understand how they are trained, adapted, and used.
Learn:
• Foundation models
• Large language models
• Parameters
• Tokens
• Context window
• Pre-training
• Fine-tuning
• Inference
• Embeddings
• Model latency
• Model accuracy
• Model cost
AWS Bedrock supports multiple foundation models, and AWS documentation maintains a list of supported models and regional availability.
Practical task:
Compare different types of foundation models based on use case: text generation, chat, summarization, embedding, image generation, and code generation.
Day 4: Understand Prompt Engineering
Prompt engineering is one of the most important GenAI skills. A good prompt can improve output quality without changing the model.
Learn:
• What a prompt is
• Zero-shot prompting
• Few-shot prompting
• Role-based prompting
• Instruction prompting
• Context prompting
• Chain-of-thought style reasoning guidance
• Prompt constraints
• Output formatting
• Prompt evaluation
Prompt structure example:
Role: You are an AWS solution architect.
Task: Explain Amazon Bedrock to a beginner.
Context: The reader understands AWS basics but not AI.
Format: Use simple bullet points.
Constraints: Keep it under 200 words.
Practical task:
Write 10 prompts for different business tasks, including summarization, email writing, Q&A, product description, interview questions, SQL generation, log analysis, report generation, chatbot response, and document extraction.
Day 5: Learn Responsible AI Basics
Generative AI should not be used blindly. You must understand its risks.
Learn:
• Bias
• Hallucination
• Toxic output
• Data privacy
• Explainability
• Fairness
• Transparency
• Human review
• Model misuse
• Security risk
• Compliance risk
The AWS Certified AI Practitioner exam guide also emphasizes responsible use of AI, ML, and generative AI technologies as a key skill area.
Practical task:
Take one GenAI use case, such as a customer support chatbot, and list possible responsible AI risks.