#1 Choice For High-Impact Careers · Beginner to Advanced

Advanced Generative and Agentic AI Program

Name: Advanced Generative and Agentic AI Program
Price: 2499.00 USD
Availability: InStock

Master Generative AI and Agentic AI with hands-on projects, LLMs, AI agents, prompt engineering, RAG pipelines, and multi-agent orchestration.

6 Months

Duration

100% Live

Mentor-led

Virtual Labs

24/7 Practice

Microsoft

Aligned Cert

Enroll Now

4.8

100% Verified Program

Advanced Generative and Agentic AI Program

Live Cohort Active

Global Certificate

Microsoft Aligned

Career Focused

Skills aligned with in-demand roles & industry needs.

Hands-on Learning

Real-world labs, projects & simulations.

Expert Instructors

Learn from industry experts with years of experience.

24/7 Support

Get help anytime from mentors and community.

menu_book Course Overview

About This Program

The next generation of software is agentic and generative. This comprehensive 6-month master program takes you from LLM fundamentals and prompt engineering to building knowledge-grounded RAG pipelines, autonomous decision-making AI agents, and collaborative multi-agent teams that automate complex business workflows.

military_tech Why Choose Us

Why Get Certification From American Tech Global

We focus on practical hands-on capability, virtual lab execution, and real mentorship to ensure you stand out in competitive global markets.

Industry Experts Live

Interactive live mentor-led sessions with industry leaders who work at top global tech firms.

Doubt Clearing Sessions

Dedicated weekly doubt clearing and personalized technical mentorship so you never stay blocked.

Assignment Evaluation

In-depth code and lab reviews with actionable personalized feedback on every milestone.

Lab For Your Practice

24/7 access to dedicated virtual lab environments and cloud infrastructure ready to use.

Industry Relevant Projects

Work on real-world datasets, scenarios, and production-grade architectures built for recruiters.

3+ Career Sessions

Resume polishing, LinkedIn profile optimization, and technical mock interviews to land your dream job.

rocket_launch Getting Started

Everything You Need to Get Started

No advanced experience required — just bring your curiosity and ambition.

Who This Is For

Built for Ambitious Professionals

Software engineers, fullstack devs, and tech leads building AI-native products

Data scientists and ML engineers expanding into autonomous multi-agent orchestration

Product managers, tech founders, and entrepreneurs creating generative AI workflows

Developers looking to master LangGraph, CrewAI, AutoGen, and enterprise RAG

Professionals preparing for Microsoft Certified: Azure AI Fundamentals and AI engineering roles

Prerequisites

What You Need to Begin

Students, developers, data professionals, or entrepreneurs eager to master modern AI

Basic computer familiarity (Python foundations taught step-by-step in Module 2)

A computer/laptop with internet access for API keys and cloud development

bolt Learning Outcomes

What You Will Master

A rigorously engineered syllabus designed in partnership with active tech leaders.

Build production LLM applications, custom copilots, and intelligent prompt systems

Master advanced prompt engineering: Zero-shot, Few-shot, Chain-of-Thought, and ReAct patterns

Create multimodal generative AI solutions combining text, vision, audio, and diffusion models

Develop enterprise RAG systems with document chunking, dense embeddings, and vector databases

Design autonomous AI agents that reason, plan tasks, use tools, and call external APIs

Build collaborative multi-agent teams using LangGraph, CrewAI, and AutoGen

Connect LLM agents to CRMs, databases, spreadsheets, and custom tools

Deploy scalable AI systems with FastAPI, Docker, guardrails, and latency/cost monitoring

Evaluate LLM outputs, mitigate hallucinations, and implement responsible AI governance

Build 4+ flagship capstone AI products with code on GitHub and live web demos

Prepare for Microsoft Certified: Azure AI Fundamentals (AI-900) and industry AI roles

construction Tools & Tech Stack

Industry-Standard Tools You'll Master

Python

LangChain

LangGraph

CrewAI

AutoGen

Python

LangChain

LangGraph

CrewAI

AutoGen

Python

LangChain

LangGraph

CrewAI

AutoGen

Python

LangChain

LangGraph

CrewAI

AutoGen

LlamaIndex OpenAI GPT-4o logo

OpenAI GPT-4o Anthropic Claude 3.7 logo

Anthropic Claude 3.7 Pinecone logo

Pinecone

ChromaDB

LlamaIndex OpenAI GPT-4o logo

OpenAI GPT-4o Anthropic Claude 3.7 logo

Anthropic Claude 3.7 Pinecone logo

Pinecone

ChromaDB

LlamaIndex OpenAI GPT-4o logo

OpenAI GPT-4o Anthropic Claude 3.7 logo

Anthropic Claude 3.7 Pinecone logo

Pinecone

ChromaDB

LlamaIndex OpenAI GPT-4o logo

OpenAI GPT-4o Anthropic Claude 3.7 logo

Anthropic Claude 3.7 Pinecone logo

Pinecone

ChromaDB

FastAPI

Streamlit

Docker

Hugging Face FastAPI logo

FastAPI

Streamlit

Docker

Hugging Face FastAPI logo

FastAPI

Streamlit

Docker

Hugging Face

FastAPI

Streamlit

Docker

Hugging Face FastAPI logo

FastAPI

Streamlit

Docker

Hugging Face FastAPI logo

FastAPI

Streamlit

Docker

Hugging Face

menu_book Course Modules

Comprehensive Curriculum

12 in-depth modules covering end-to-end concepts, hands-on lab exercises, and capstone milestones.

Understand what generative AI is, how it evolved and where it creates business value.

AI, ML, deep learning and generative AI explained

History and evolution of generative models

Generative AI use cases across industries

Capabilities, limitations and hallucinations

Lab: exploring leading generative AI platforms

Set up a professional AI engineering environment and the Python skills to use it.

Python essentials for AI engineers

Working with APIs, JSON and async code

NumPy, Pandas and data handling for AI

Jupyter, VS Code, Git and environment management

Lab: your first LLM-powered Python app

Learn the neural network foundations behind every modern generative model.

Neural networks and backpropagation

Embeddings and representation learning

The attention mechanism

Transformer architecture in depth

Lab: building a mini-transformer in PyTorch

Work hands-on with commercial and open-source LLMs, and choose the right one for the job.

How LLMs are pre-trained and aligned

Commercial vs open-source models

Tokens, context windows and sampling parameters

Structured outputs and function calling

Lab: benchmarking models for a real use case

Design prompts that are reliable, testable and production-ready.

Prompt design principles

Zero-shot, few-shot and chain-of-thought prompting

System prompts, roles and guardrails

Prompt evaluation and iteration

Lab: building a prompt library for a business workflow

Generate and understand images, audio and video alongside text.

Diffusion models and image generation

Vision-language models and image understanding

Speech-to-text and text-to-speech

Video generation and editing workflows

Lab: building a multimodal content assistant

Ground LLMs in your own data with robust retrieval pipelines.

RAG architecture and when to use it

Chunking strategies and embeddings

Vector databases: Pinecone, Chroma and pgvector

Hybrid search, re-ranking and evaluation

Lab: enterprise knowledge-base assistant

Adapt models to your domain with efficient fine-tuning techniques.

When to fine-tune vs prompt vs RAG

Preparing instruction datasets

Parameter-efficient fine-tuning with LoRA and QLoRA

Evaluating fine-tuned models

Lab: fine-tuning an open-source model

Move from chatbots to autonomous agents that reason, plan and take action.

What makes an AI system agentic

Reasoning and planning patterns (ReAct, plan-and-execute)

Tool use and function calling

Memory: short-term, long-term and episodic

Lab: building a single-agent research assistant

Build agents with the leading frameworks and connect them to real systems.

LangChain and LangGraph

CrewAI and AutoGen

Model Context Protocol (MCP) and tool integrations

No-code agent workflows with n8n

Lab: an agent that automates a business process

Design teams of agents that collaborate safely and reliably.

Multi-agent architectures and communication patterns

Orchestration, routing and supervision

Human-in-the-loop approvals

Agent evaluation, observability and debugging

Lab: multi-agent customer operations system

Ship generative AI to production responsibly and showcase a flagship project.

Deploying AI apps with FastAPI, Docker and the cloud

LLMOps: monitoring, cost and latency optimisation

AI safety, ethics and governance

Portfolio, interviews and career paths in AI engineering

Flagship capstone: production-grade agentic AI application

deployed_code Real-World Portfolio

Projects That Build Your Portfolio

You don't watch tutorials. You build autonomous AI agents and ship production AI products.

Enterprise Document RAG Pipeline

Build a production knowledge retrieval engine with semantic chunking, Pinecone vector search, and citation sources.