Who is this for?
Developers and ML engineers building LLM-powered applications.
Syllabus
1. GenAI Foundations and Model Selection (5 h)
- Foundation model landscape
- Choosing and comparing models
- Inference parameters, tokens and cost
- Hands-on: Compare models in Bedrock
2. Prompt Engineering (5 h)
- Prompt patterns and structured output
- System prompts and context management
- Prompt versioning and testing
- Hands-on: Build a prompt library
3. Embeddings and Vector Stores (5 h)
- Embeddings and similarity search
- OpenSearch, pgvector and Knowledge Bases
- Chunking and indexing strategies
- Hands-on: Index a document set
4. Retrieval-Augmented Generation (7 h)
- RAG architecture and pipelines
- Hybrid search and re-ranking
- Grounding and citations
- Hands-on: Build an enterprise Q&A bot
5. Agents and Tool Use (6 h)
- Bedrock Agents and action groups
- Function calling and orchestration
- Multi-step workflows with Step Functions
- Hands-on: Agent that calls business APIs
6. Customization (4 h)
- Fine-tuning vs RAG vs prompting
- Model customization options on AWS
- Evaluation datasets
7. Safety, Guardrails and Governance (5 h)
- Bedrock Guardrails
- PII protection and data privacy
- Responsible-AI controls and auditing
- Hands-on: Apply guardrails to an app
8. Evaluation, Monitoring and Optimization (6 h)
- Automated and human evaluation
- Observability and tracing
- Caching, batching and cost optimization
- Hands-on: Evaluation harness
9. Deployment and Capstone (5 h)
- Serverless and containerized deployment
- CI/CD for GenAI apps
- Capstone build and review
- Hands-on: Capstone project
After this course you will be able to
- Integrate foundation models into applications using Amazon Bedrock
- Design and implement RAG with vector stores
- Build agents and tool-using workflows
- Apply prompt engineering, evaluation and guardrails
- Deploy, monitor and optimize GenAI workloads for cost and latency
- Secure and govern GenAI applications
Tools & topics
Amazon BedrockBedrock AgentsKnowledge BasesOpenSearchSageMakerLambdaPythonLangChain
Projects
- Enterprise document Q&A (RAG) application
- Tool-using AI agent with guardrails
Assessment
Weekly build assignments, evaluation review and capstone assessment.
What is included
- Live instructor-led online sessions
- Hands-on labs and assignments
- Session recordings after each class
- Doubt-solving support
- Course completion certificate
- Small interactive batches
- Unpaid internship opportunities
- Session recordings after each class
- Small interactive batches
- Unpaid internship opportunities for practical exposure
Your trainer
Ameya Vaidya
Cloud Trainer: AWS, Azure & GCP
Ameya started in IT more than 16 years ago and has spent over a decade in the cloud. He holds six AWS and two Azure certifications, is an AWS Community Builder and a speaker, and has trained over 5,000 students. Ask him about security, networking or AI and expect real-world examples alongside the theory.
- 6 AWS and 2 Azure certifications
- 16+ years in IT, 10+ years in cloud
- Expertise in security, networking and AI
- Trained 5,000+ students
- AWS Community Builder and speaker
What our AWS Generative AI Developer – Professional students say
FAQ
Can a beginner join AWS Generative AI Developer – Professional?
Python programming, AWS fundamentals and basic ML/LLM awareness. AI Practitioner or Developer Associate recommended.
Will there be a free demo?
Yes. Book a free demo, attend the live session and decide after you have seen how we teach.
Do you guarantee a job after this course?
No. We do not make guaranteed-placement claims. We train you to be capable, help with resume and career guidance, and offer unpaid internship opportunities.
What is the fee and batch schedule?
Our counselor shares the current fee and batch details. Book a free demo or WhatsApp +91-9156091020.
Not sure which course is right for you?
Talk to our counselor. It is free, and we will tell you honestly whether a course fits your goals.