GenAI Vs Agentic AI?
Training Overview:
This course is designed to introduce generative AI to software developers interested in leveraging large language models without fine-tuning. The course provides an overview of generative AI, planning a generative AI project, getting started with Amazon Bedrock, the foundations of prompt engineering, and the architecture patterns to build generative AI applications using Amazon Bedrock and LangChain.
Key Objective:
- Understanding of complete generative AI and how it aligns with machine learning.
- Define the importance of generative AI and explain its potential risks and benefits
- Complete understanding of how AWS Bedrock works
- Define prompt engineering and apply general best practices when interacting with FMs.
- Implementation of AI Agents using AWS Bedrock
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Course Structure
- What is Generative AI (Simple explanation vs ML)
- What are Foundation Models (FMs)
- Overview of AWS Generative AI services (focus on Amazon Web Services ecosystem)
- Real-world use cases (Business + IT)
- Demo: Implementation of GenAI use case
- Identifying good use cases (what works / what doesn’t)
- Basics of project planning (simple lifecycle)
- Introduction to Responsible AI:
- Bias, safety, hallucination (in simple terms)
- Introduction to guardrails in Amazon Bedrock
- Activity: Define the right set of use-cases
- What is prompting in AI?
- Understanding of Prompt Techniques:
- Zero-shot
- Few-shot
- Role prompting
- Common mistakes that users can make while using the prompt
- Hands-on: Improve outputs using prompts
- What is Amazon Bedrock (simplified)
- Understanding of Model selection basics
- Using Bedrock Playground & APIs (with demo)
- Understanding of different Parameters (temperature, tokens – explained)
- Demo: Optimizing Slogan Generation with Amazon Bedrock
- Why data is important in GenAI applications
- Introduction to embeddings (intuitive explanation)
- What is RAG (Retrieval Augmented Generation)
- Understanding RAG Use Cases
- Demo: Build a RAG based use-cases
- Common GenAI applications:
- Summarization
- Chatbots
- Content generation
- When to use which pattern
- Demo: Building a chatbot using different patterns
- Why use various frameworks like LangChain
- Key components:
- Chains
- Prompts
- Memory
- Simple integration with AWS
- Demo: Building a Context-aware chatbot
- From prompts → apps → agents
- What makes an AI “agent”
- Types of agents:
- Workflow-based
- Autonomous
- Real-world examples
- Use cases in actual business scenarios
- Introduction to agents in Amazon Bedrock
- Exploring Agent architecture
- Understanding Tools & integrations used to build Agents
- Demo: Building a task-based agent
- Build a simple AI solution:
- Use case + prompts + data + agent concept
- Group activity or guided build
- Best practices for deployment:
- Cost awareness
- Monitoring basics
- Final discussion & next steps
Training Details
Training Schedule
Timings: 07:30 to 09:30 PM IST
Days: Monday to Friday
Total Duration: 20 hours
Date: 11-May-26
Mode: Online
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Testimonials
Ankur is best tutor for OIC . Where he teaches from the basic .I'm very much thankful to Ankur . Because of him I got knowledge on Oic ,VBCS .He explain with use cases . Very quick to understand
Sai Abhiram Bethi
Ankur is very detailed in training, it’s really helpful for the beginners to become proficient in OIC.
Jagan Mohan
It’s a great blog and community for those who want to learn OIC, VBCS , PCS and other advanced technologies. Thanks you so much for the contribution Mr. Ankur Jain
krishna d
Ankur is an Expert Trainer, he clears all the doubts with examples and Lab training and it worth the money and time, I would recommend all the enthusiasts to approach him. He is a Genuine Trainer and down to earth person.
Shaik Saddiqulla
The training was good. All the topics covered and fact is this all topics covered in well manner. if you are fresher and no idea about basics that is also covered by Ankur. for me lot of learning from this course. really worth it Thank Ankur for your valuable efforts which one you have given to us in this whole training.