Agentic AI Claude Training Program

In this module, you will build a strong foundation in Artificial Intelligence and learn how it is used in real-world applications. The focus is on explaining concepts in a simple and beginner-friendly way so you can easily relate them to everyday applications.

What you will learn:

• What Artificial Intelligence (AI) is and how it is used in apps like chatbots, recommendations, and automation
  • Clear difference between AI, Machine Learning, and Deep Learning with simple examples
  • How machines identify patterns and learn from data step-by-step
  • Basic understanding of how AI models are trained and improved over time
  • Real-world use cases of AI in industries like healthcare, business, and e-commerce

This module introduces modern AI systems that go beyond answering questions and can perform tasks independently. You will understand how AI is evolving into increasingly intelligent, action-driven systems.

What you will learn:

• What Agentic AI is and how it can plan, decide, and execute tasks automatically
  • Understanding Generative AI and how it creates content like text, images, and ideas
  • Simple working of tools like ChatGPT and similar AI systems
  • Introduction to large language models (LLMs) and how they understand context
  • Basics of prompt engineering to get accurate and useful AI outputs

In this module, you will get practical exposure to Claude AI and learn how to use it effectively for different tasks. This will help you become comfortable working with AI tools in real scenarios.

What you will learn:

• What Claude AI is and how it differs from other AI tools in terms of usage and output
  • Step-by-step guidance to start using Claude confidently
  • How to write clear and structured prompts for better results
  • Techniques to refine and improve AI-generated responses
  • Using Claude for tasks like writing, research, analysis, and idea generation

In this module, you will focus on building a content system driven by strong prompt design. The goal is to understand how structured prompts can control AI outputs effectively.

What you will do:

• Learn prompt engineering in adaptation, where prompts are refined based on output quality
  • Create dynamic prompts that change tone, format, and intent based on user needs
  • Understand how tokenization impacts prompt size and response quality
  • Build a content system that generates blogs, captions, and structured outputs
  • Learn basic deployment concepts for prompts using the Claude interface
  • Practice creating reusable prompt templates for consistent content generation

In this module, you will design a fully functional AI-powered communication assistant focused on email automation and response handling.

What you will do:

• Build an AI agent that reads incoming emails and generates contextual replies
  • Create workflows where Gmail integrates with AI to auto-draft and send responses
  • Use AI note-takers to convert meetings into actionable email summaries
  • Implement context management to track ongoing conversations across threads
  • Train your system to handle professional tone, urgency, and intent detection
  • Explore how Claude can be used to maintain clarity in long email conversations

In this module, you will build a system that improves accuracy by combining AI with external knowledge sources.

What you will do:

• Learn and implement RAG (Retrieval-Augmented Generation) for real-world use
  • Build a system where AI retrieves relevant information before generating answers
  • Understand how tokenization works when processing long documents and datasets
  • Create a knowledge assistant that answers questions from uploaded files or data
  • Improve response accuracy by grounding AI outputs in real information
  • Explore use cases like FAQs, internal knowledge bases, and documentation assistants

In this module, you will learn how multiple AI agents can work together as a team to solve complex tasks.

What you will do:

• Understand the AutoGen and multi-agent collaboration systems
  • Build a system where different agents handle research, writing, and validation separately
  • Learn how agent lifecycle works (task → execution → feedback → improvement)
  • Implement collaboration between agents to improve output quality
  • Handle long conversations using structured context sharing between agents
  • Use Claude as a central intelligence layer for coordinating agent tasks