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eMexo Technologies
Gen AI & Agentic AI Training in Electronic City Bangalore
Curriculum
11 Sections
55 Lessons
10 Weeks
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Introduction to Generative & Agentic AI
5
1.1
What is Generative AI?
1.2
Evolution from Traditional AI → Generative AI → Agentic AI
1.3
Key Concepts: Generative Models, Reasoning, Autonomy, Goal-driven AI
1.4
Real-world Applications: ChatGPT, Copilot, Gemini, AutoGPT, ReAct Agents
1.5
Overview of Large Language Models (LLMs)
Foundation Models & LLM Architecture
5
2.1
Transformer Architecture Deep Dive
2.2
Tokenization, Embeddings, Attention Mechanism
2.3
Understanding Model Parameters and Scaling Laws
2.4
Fine-tuning vs. Prompt Engineering vs. RAG
2.5
Popular Models: GPT, Llama, Mistral, Claude, Gemini
Prompt Engineering & Generative Techniques
5
3.1
Types of Prompts: Zero-shot, Few-shot, Chain-of-Thought
3.2
Context Management and Role-based Prompting
3.3
Structured Output Generation (JSON, SQL, HTML)
3.4
Multi-turn Conversation Handling
3.5
Prompt Optimization and Evaluation Metrics
Generative AI for Text, Image & Code
5
4.1
Text Generation: Summarization, Translation, Content Creation
4.2
Image Generation: DALL·E, Midjourney, Stable Diffusion
4.3
Code Generation: GitHub Copilot, Code Llama, Replit AI
4.4
Multi-modal Models: Gemini, GPT-4o, CLIP
4.5
Hands-on Labs: Text & Image Generation Projects
Agentic AI – Concepts & Frameworks
5
5.1
What are AI Agents?
5.2
Architecture: Planner, Memory, Tools, Feedback Loops
5.3
Autonomous vs. Semi-Autonomous Agents
5.4
Frameworks: LangChain, CrewAI, AutoGPT, ReAct, Semantic Kernel
5.5
Designing Goal-Oriented AI Agents
Building AI Agents (Hands-on)
5
6.1
Tool Use: API Calling, Web Search, Knowledge Retrieval
6.2
Integrating Memory and Context
6.3
Multi-Agent Collaboration
6.4
Task Planning and Decomposition
6.5
Project: Multi-Agent Workflow System
Retrieval-Augmented Generation (RAG)
5
7.1
What is RAG and Why It’s Needed
7.2
Vector Databases (Pinecone, FAISS, Chroma)
7.3
Document Chunking and Embeddings
7.4
Enterprise Chatbot using RAG
7.5
Project: Document-based AI Assistant
Fine-Tuning and Custom Model Development
5
8.1
Supervised Fine-Tuning (SFT)
8.2
Parameter-Efficient Fine-Tuning (PEFT, LoRA)
8.3
Dataset Preparation
8.4
Model Evaluation & Deployment
8.5
Project: Fine-tune an Open-source Model
Integrations & Deployment
5
9.1
APIs and Cloud Integration (OpenAI, Anthropic, Hugging Face)
9.2
AI Web Apps with Streamlit / Gradio
9.3
Tool Integration (Email, Slack, DBs)
9.4
Containerization & Model Serving
9.5
Deployment on AWS / Azure / GCP
Responsible AI & Ethics
5
10.1
Bias, Hallucination, and Transparency
10.2
Privacy and Data Governance
10.3
Human-AI Collaboration
10.4
Safety Guardrails and Monitoring
10.5
Future Trends: Agent Swarms, Self-improving Systems
Capstone Project
Students design and present a fully functional Agentic AI System such as:
5
11.1
Research Assistant Agent
11.2
Business Report Generator
11.3
Code Reviewer Agent
11.4
Automated Marketing Content Creator
11.5
AI Workflow Automation System
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