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Transforming people and organisations around the globe

HAC

Generative AI, AI Agents and Agentic AI &(with Azure Cloud)

Master the latest AI tools and trends to become an in-demand Gen AI & Agentic AI Developer/Engineer.

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4.8
2147 rating
619 students
Sophia Jaymes
By Nitesh
  • Last updated 12/03/2023
  • English
  • Certified Course
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Course Overview

The Generative AI Program is designed to cover essential areas, including mastering Python, LLMs, RAG and Agentic AI. Participants will gain expertise in implementing and creating GenAI based applications, with deploying applications in Azure, and ensuring robust data security.

This curriculum provides a comprehensive and hands-on understanding of Generative AI, AI agents and Agentic AI applications, upskilling and preparing you to become a proficient Generative AI Engineer.

Ignite your Generative AI career with HAC. Enroll today and embark on a transformative learning journey!

Course Objectives

This course aims to equip learners with a strong foundation and hands-on expertise in Generative AI, Large Language Models, and Agentic AI. Participants will explore the inner workings of LLMs, master prompt engineering, build intelligent AI agents, and implement advanced pipelines like RAG and MCP. The focus is on developing practical, ethical, and scalable AI solutions using modern tools such as LangChain, LLaMA, Groq, Agno, and Chromadb.

  • Get a deep dive into essential subjects like Azure Cosmo DB, Azure Synapse, Azure Databricks, Azure Stream Analytics, Azure HDInsight, and Azure Data Factory.
  • Understand the fundamentals and evolution of AI & Generative AI.
  • Learn the architecture and working of Large Language Models (LLMs).
  • Master transformers, tokenization, and attention mechanisms.
  • Design effective prompts and optimize prompt performance.
  • Build LangChain-based AI workflows and pipelines.
  • Work with vector databases and similarity search using Chromadb.
  • Implement Retrieval-Augmented Generation (RAG) systems.
  • Develop intelligent single and multi-agent systems using Agentic AI.
  • Fine-tune and evaluate AI models with LoRA and Agno frameworks.
  • Apply responsible and ethical AI practices in real-world projects.

Hurry up and join our Microsoft Azure Data Engineer Certification Course today to propel your career to greater heights.

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10 Weekends Schedule: GenAI Coverage

A comprehensive hands-on journey from AI fundamentals to enterprise-grade Agentic AI systems — delivered over 10 action-packed weekends.

3
Week 3 – AI & Generative AI Fundamentals
Saturday · 9 May
Understand how AI evolved from rule-based systems to Machine Learning, Deep Learning, and Generative AI. Explore the difference between predictive and generative AI, with use cases across healthcare, finance, education, and software development.
Topics Covered
  • Introduction to Artificial Intelligence
  • Introduction to Generative AI
  • Evolution of Generative AI
  • Traditional AI vs Generative AI
  • Real-world applications of GenAI
  • GenAI vs AI Agents vs Agentic AI
Hands-On
  • Explore ChatGPT and Microsoft Copilot
  • Compare outputs from AI tools
  • Test different AI prompts
  • Analyze AI-generated responses
Sunday · 10 May
Learn the different architectures used in GenAI systems and understand why Transformers became the foundation for modern LLMs. Study how ChatGPT works internally using tokens, embeddings, and attention mechanisms.
Topics Covered
  • Types of Generative AI Models
  • GANs, VAEs, Transformers, Diffusion Models
  • Introduction to LLMs
  • Vector Databases Overview
  • Introduction to RAG
  • Building a Custom GPT
Hands-On
  • Create a Custom GPT using ChatGPT
  • Build a Resume Improver GPT
  • Create a simple AI assistant
  • Practice prompt testing
4
Week 4 – Prompt Engineering
Saturday · 16 May
Learn how prompt engineering improves AI responses and understand how structured prompts generate better outputs. Study enterprise prompting strategies and optimization techniques.
Topics Covered
  • Introduction to Prompt Engineering
  • Types of Prompts
  • Zero-shot Prompting
  • One-shot Prompting
  • Few-shot Prompting
  • Chain-of-Thought Prompting
  • CRAFT Framework
  • Prompt Evaluation
Hands-On
  • Design prompts for business use cases
  • Build structured prompts
  • Compare different prompting methods
  • Prompt refinement exercises
Sunday · 17 May
Understand how prompts influence model behavior and how businesses use prompt engineering in production systems.
Topics Covered
  • Prompt Optimization
  • Bias Detection in Prompts
  • Prompt Testing Techniques
  • Enterprise Prompting
  • Vibe Coding
Hands-On
  • AI Language Translator
  • AI Blog Generator
  • Prompt performance evaluation
  • Vibe code an app
5
Week 5 – LangChain
Saturday · 23 May
Learn how LangChain helps developers build AI-powered applications using reusable chains, prompts, and workflows. Understand modular AI application development.
Topics Covered
  • Introduction to LangChain
  • LangChain Ecosystem
  • LangChain Installation
  • Prompt Templates
  • Chains
  • Output Parsers
Hands-On
  • Install LangChain
  • Create Prompt Templates
  • Build simple chains
  • Parse structured outputs
Sunday · 24 May
Study how LangChain integrates with multiple LLM providers and enables workflow orchestration.
Topics Covered
  • Calling LLMs using LangChain
  • Groq Setup
  • Ollama Setup
  • LangChain Workflows
  • Enterprise Scenarios
Hands-On
  • Connect OpenAI APIs
  • Build AI Blog Generator
  • Create conversational workflow
  • Build AI automation scenarios
6
Week 6 – LLMs & Transformers
Saturday · 30 May
Understand how Large Language Models are trained and why transformers revolutionized Natural Language Processing. Learn how tokens are generated, how embeddings work, and how self-attention helps models understand context.
Topics Covered
  • Introduction to LLMs
  • Traditional Models vs LLMs
  • GPT, Claude, Gemini, LLaMA
  • Transformer Architecture
  • Encoder & Decoder
  • Tokenization
  • Embeddings
  • Self-Attention Mechanism
  • Positional Encoding
Hands-On
  • Tokenization examples
  • Embedding visualization
  • Compare outputs from different LLMs
  • Explore Hugging Face models
Sunday · 31 May
Understand practical LLM tuning concepts like temperature, token limits, and hallucinations. Study reasoning models and how Agentic AI systems make decisions autonomously.
Topics Covered
  • Working with ChatGPT & Copilot
  • Context Window
  • Temperature Settings
  • LLM Hallucinations
  • Reasoning Models
  • Agentic AI Scenarios
Hands-On
  • Build an AI Email Generator
  • Test hallucination scenarios
  • Compare reasoning vs standard models
  • Real-world Agentic AI workflows
7
Week 7 – Vector Databases
Saturday · 6 Jun
Learn how Vector Databases store embeddings and enable semantic search. Understand cosine similarity, Euclidean distance, and dot product calculations.
Topics Covered
  • Introduction to Vector Databases
  • Traditional DB vs Vector DB
  • Embeddings
  • Similarity Search
  • High-Dimensional Vector Space
  • Distance Metrics
Hands-On
  • Generate embeddings
  • Similarity search experiments
  • Semantic retrieval testing
Sunday · 7 Jun
Understand how vector stores power RAG systems and enterprise AI search applications.
Topics Covered
  • ChromaDB Installation
  • ChromaDB Operations
  • Add, Update, Delete, Query
  • Metadata Filtering
Hands-On
  • Setup ChromaDB
  • Build semantic search system
  • Create vector search pipeline
  • Query filtering exercises
8
Week 8 – Retrieval Augmented Generation (RAG)
Saturday · 13 Jun
Understand how RAG improves AI responses using external knowledge retrieval. Study the complete RAG architecture from ingestion to response generation.
Topics Covered
  • Introduction to RAG
  • RAG Architecture
  • Retrieval + Generation Pipeline
  • Document Loaders
  • Text Splitters
  • Embedding Models
Hands-On
  • Load documents
  • Split large text files
  • Generate embeddings
  • Build retrieval pipeline
Sunday · 14 Jun
Learn how businesses use RAG systems for enterprise chatbots and knowledge assistants.
Topics Covered
  • Streamlit UI
  • Retrieval Systems
  • Answer Generation
  • Real Estate RAG Agent
  • E-Commerce RAG
Hands-On
  • Build Real Estate RAG Agent
  • Build E-Commerce RAG
  • Create Streamlit chatbot UI
  • Test document-based Q&A
9
Week 9 – AI Agents & Agentic AI
Saturday · 20 Jun
Understand how autonomous AI agents work and how they coordinate tasks. Learn multi-agent collaboration systems used in enterprise automation.
Topics Covered
  • Introduction to AI Agents
  • Single-Agent vs Multi-Agent Systems
  • Introduction to Agentic AI
  • AI Agents vs Agentic AI
  • Multi-Agent Design Patterns
  • Route Agents
Hands-On
  • Agent architecture design
  • Multi-agent communication
  • Workflow orchestration exercises
Sunday · 21 Jun
Study how reasoning agents analyze goals, make decisions, and execute tasks autonomously.
Topics Covered
  • Building Agents using Llama & Agno
  • Reasoning Agents
  • Multimodal Agents
  • Multi-Agent Systems
Hands-On
  • Build first AI Agent
  • Build Reasoning Agent
  • Build Multimodal Agent
  • Multi-agent workflow project
10
Week 10 – MCP & Agent Communication
Saturday · 27 Jun
Learn how Model Context Protocol enables communication between AI systems, tools, and agents. Understand enterprise integration architecture for AI systems.
Topics Covered
  • Introduction to MCP
  • Prebuilt MCP Servers
  • A2A Protocol
  • MCP Architecture
Hands-On
  • Explore MCP workflows
  • Connect AI tools
  • Agent communication exercises
Sunday · 28 Jun
Understand how enterprises use MCP to build scalable AI ecosystems.
Topics Covered
  • Build First MCP Server
  • Agent Integration
  • Enterprise AI Integration
  • MCP Workflow Testing
Hands-On
  • Build custom MCP server
  • Connect AI agents
  • Test communication pipelines
  • Mini enterprise integration project
11
Week 11 – Evaluation & Fine-Tuning
Saturday · 4 Jul
Learn how AI systems are evaluated for quality, safety, and enterprise readiness. Study hallucination reduction and responsible AI practices.
Topics Covered
  • Agentic AI Evaluation
  • Functional Evaluation using Agno
  • Safety & Guardrails
  • Operational Metrics
  • Performance Evaluation
Hands-On
  • AI evaluation testing
  • Safety rule implementation
  • Guardrail testing
  • Performance benchmarking
Sunday · 5 Jul
Understand how organisations customise models using parameter-efficient fine-tuning.
Topics Covered
  • Introduction to Fine-Tuning
  • LoRA & QLoRA
  • Fine-Tuning with Unsloth
  • Llama Fine-Tuning
Hands-On
  • Fine-tune a Llama model
  • Hyperparameter testing
  • Evaluate tuned models
  • Compare base vs tuned model outputs
12
Week 12 – Azure OpenAI, N8N & Final Capstone
Saturday · 11 Jul
Learn workflow automation and AI orchestration using N8N and Azure OpenAI services.
Topics Covered
  • AI Workflows using N8N
  • Marketing Automation
  • AI Content Automation
  • Azure OpenAI
Hands-On
  • Build automation workflows
  • Create AI chatbot flows
  • AI content generation pipelines
  • Azure AI integrations
Sunday · 12 Jul
Bring it all together with a comprehensive capstone project, portfolio building, and career preparation.
Topics Covered
  • Final Capstone Project
  • AI Chat Agent
  • Multi-Agent System
  • Portfolio Building
  • Resume Preparation
  • Interview Preparation
Outcome
  • Complete AI portfolio ready
  • Job-ready resume & LinkedIn
  • Interview Q&A practice
  • Career transition roadmap

Hands-On Labs & Agentic Scenarios

  • Building Custom GPTs using ChatGPT
  • Creating & working with SQL Data Analyst GPT
  • Creating Resume Improver GPT
  • AI Email Generator
  • AI Blog Generator with OpenAI SDK
  • Text Summarizer with Copilot
  • AI Language Translator
  • Code Explainer [with ChatGPT & Copilot]
  • Spam Detection with Hugging Face
  • Context window, Temperature
  • LLM Hallucinations
  • Langchain installation
  • Groq and Ollama setup
  • Prompting with Langchain
  • Calling LLMs from Langchain
  • Prompt Templates and Chains
  • Output Parser
  • Chromadb installation/set up
  • Chromadb operations
  • Add, Update, Delete and Query
  • working with Metadata Filtering
  • Building your first agent with LLama & Agno
  • Building Reasoning Agents with Agno
  • Multimodal Agents
  • Building MCP Server
  • Building Multi-Agent system
  • LLM evaluation and fine-tuning

Capstone Real-time Projects

  • Project-1: Creating SQL Data Analysis Custom GPT using ChatGPT
  • Project-2: Training Deck using NotebookLM and Gamma AI
  • Project-3: Property buying decision using ChatGPT, Gemini, Preplexity
  • Project-4: Real Estate RAG Agent
  • Project-5: E-Commerce RAG
  • Project-6: AI chatbot using N8N
  • Project-7: Invoice automation using N8N
  • Project-8: Multi-Agentic system
  • Project-9: Chat Agent with Azure OpenAI
  • Projetc-10: AI agent with Microsoft Phi SLM
  • Project-11: Design Website Landing Page using Lovable AI
  • Project-12: Project Tracker Automation with Notion & Zapier
  • Project-13: Retail Data Insights using Pandas & Matplotlib
  • Project-14: Uber Eats EDA using Pandas, Statistics & Seaborn

Bonuses

  • GenAI & Agentic AI Interview questions & answers
  • Real-time scenarios and solutions
  • AWS certified Generative AI Developer certification support
  • Resume/CV preparation
  • Building Project Portfolio
  • LinkedIn profile optimization
  • Placement Assistance
  • On-Job Support

Target Jobs

  • Generative AI Engineer/Developer
  • Agentic AI Specialist/Consultant
  • AI Engineer
  • LLM Engineer
  • Prompt Engineer
  • AI Automation Engineer/Consultant

Program Outcome

  • Transition your career to GenAI & Agentic AI roles.
  • Land high-paying AI and GenAI jobs globally.
  • Up to ~300X times increment in your current salary.
  • Switch to Top IT & Product based companies.
  • Have a secured career and work in a Future trend job.
  • Become a top & highly paid IT professional.
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FAQ

A:The HAC program is a comprehensive, practical training course designed to help learners master Generative AI, Large Language Models (LLMs), and Agentic AI through real-world, hands-on projects using tools like LangChain, LLaMA, Agno, Groq, and Chromadb.

A: This course is ideal for students, developers, data scientists, AI enthusiasts, and professionals who want to gain in-depth practical skills in Generative AI, LLMs, and Agentic AI applications.

A: Basic programming knowledge (preferably in Python) is recommended, but the course is structured to guide learners from foundational AI concepts to advanced implementations step by step.

A: Unlike theoretical programs, HAC is hands-on and project-driven. Learners build real AI systems such as RAG pipelines, multi-agent architectures, and LangChain-based applications using modern frameworks and tools.

A: You’ll work with OpenAI APIs, Hugging Face, LangChain, LLaMA, Groq, Agno, and Chromadb, along with frameworks for transformers, vector databases, and prompt optimization.

A: By the end, participants will be able to design, develop, and deploy Generative AI models, AI agents, and multi-agent systems, and understand how to fine-tune, evaluate, and scale them ethically.

A:Learners will build prompt-based AI models, LangChain-powered chatbots, RAG pipelines, vector database search systems, and Agentic AI applications using LLaMA and Agno frameworks.

A: The course is divided into 12 modules, starting with AI fundamentals and progressing to advanced topics like transformers, prompt engineering, RAG systems, multi-agent systems, and ethics in GenAI.

A: Yes. Participants who complete all modules and projects successfully will receive a certificate of completion, validating their expertise in Generative AI and Agentic AI technologies.

A:Graduates will gain in-demand skills to pursue roles such as AI Engineer, Prompt Engineer, LLM Developer, Data Scientist, or AI Researcher, and will be equipped to contribute to real-world AI innovation and enterprise projects.
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Instructor

Nitesh
GenAI Trainer & AI Automation Expert · London, UK

Nitesh is based in London and comes with strong hands-on expertise in Generative AI, AI automation, and data-driven analytics. He has trained many students globally on GenAI concepts, AI model development, and practical AI implementation using the latest AI tools and technologies. He has also successfully trained students from UK universities and learners across 30+ countries.

His core expertise includes AI-driven analytics and real-world AI projects involving NLP, LangChain, Large Language Models (LLMs), AI agents, and AI automation solutions that enhance reporting, intelligent decision-making, and enterprise business workflows.

With a practical, implementation-focused, and industry-oriented teaching approach, Nitesh is passionate about helping students build real-world AI skills aligned with current global market demand.

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  • Enrolled60
  • Lectures50
  • Skill LevelBasic
  • LanguageEnglish
  • Quizzes10
  • CertificateYes
  • Pass Percentage95%
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