Generative AI

Master Agentic AI by creating intelligent agents, generative workflows,and LLM-based applications with LangChain,CrewAI, AutoGPT,and advanced AI technologies.

Why Choose This Program?

Generative AI is transforming the future of technology by enabling intelligent applications that can create content, automate tasks, and solve complex business problems. At Sree Technologies, you'll master Large Language Models (LLMs), Prompt Engineering, Retrieval-Augmented Generation (RAG), AI agents, and leading AI frameworks through hands-on projects and real-world use cases. Our industry-focused curriculum, combined with AI-powered tools and expert mentorship, prepares you to build production-ready Gen AI applications and launch a successful career in Artificial Intelligence.

Course Curriculum

Introduction & Foundations

Introduction
What is Generative AI?
What is Agentic AI?
Generative AI vs Agentic AI

Fundamentals of Generative AI & LLMs

Basics of Generative AI
What are LLMs?
Key Concepts
Tokenization
Embeddings
Vocabulary
Attention
Transformer Architecture
Self-Attention Mechanism
Multi-head Attention
Cross Attention
Encoder–Decoder

Working with LLMs

Open Source vs Closed Source LLMs
Hugging Face Ecosystem
Model Loading
Model Parameters
Model Weight Formats (.pth, safetensors, onnx)
Model Size Calculation and License
Multimodal LLMs: Text, Audio (ASR, TTS), Image, Video

Prompt Engineering & Control

Prompts and Context
Prompting Techniques
Guardrails

Retrieval-Augmented Generation (RAG)

What is RAG?
LLM Hallucination: Causes and Mitigation
When to Use RAG
Components of RAG

Advanced RAG Techniques

Corrective RAG (CRAG)
Self-RAG with Reflection
Graph RAG
Hybrid RAG(Semantic + Keyword)

Graph-based Knowledge and Retrieval

Graph Fundamentals-Nodes, Edges
Ontology Design
GraphDBs

LangChain Framework

Introduction to LangChain
Chains, Prompts, and Templates
Memory Systems and Conversation Flow
Memory Types: Short-term, Long-term, Episodic
Persistence Strategies
Basic Document Loaders and Text Splitters

Agentic AI Principles

What is Agentic AI?
AI Agents vs Agentic AI
Agentic AI Frameworks

Production & Final Project

Agentic AI using LangGraph
LangChain vs LangGraph
LangGraph Components
Workflow Types
Memory & State Management
Observability with LangSmith

Model Context Protocol (MCP)

MCP Fundamentals and Architecture
MCP Server and Client Implementation
Tool Integration through MCP
MCP vs Traditional Integration Patterns

Agent-to-Agent Communication

Agent-to-Agent Communication Fundamentals
Orchestration Patterns of Manager-Worker
Orchestration Patterns of Peer-to-Peer

Fine-Tuning and Quantization

When to Use Fine-tuning vs Prompt Engineering
Parameter-Efficient Fine-Tuning (PEFT)
LoRA
QLoRA
Quantization Techniques

Model Serving , Deployment & Projects

Model Serving Frameworks
AWS/Vercel/Render basics
Docker deployment
Environment variables and secrets
GitHub Actions basics
Production debugging
Resume and portfolio preparation
Final Projects
Domain-Aware LLM Chatbox using open-Source
Customer Support Assistant Powered by Retrieval-Augmented Generation
Agentic AI Advisor for Healthcare Guidance and Decision support

CAREER OUTCOMES

Roles You Can Target

Gen AI Engineer
AI Prompt Engineer
LLM Developer
Agentic AI Developer
AI Product Engineer
Conversational AI Developer

ELIGIBILITY

Who Should Join?

AI/ML professionals seeking advanced skills
Software developers wanting to pivot into AI
Data scientists exploring generative models
Product managers working in AI-driven products