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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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9 Weeks Schedule: GenAI Coverage

A comprehensive hands-on journey from AI fundamentals to enterprise-grade Agentic AI systems — delivered over 9 action-packed weekends.

1
Prompt Engineering, Custom GPTs & Google Gems
Saturday · Day 1
Learn how to engineer prompts that reliably produce high-quality AI outputs. Understand the core differences between zero, one, and few-shot approaches. Study structured frameworks like CRAFT and RISE for enterprise prompting, and design responsible, role-based personas for business scenarios.
Topics Covered
  • What is Prompt Engineering and why it matters
  • Zero-shot, one-shot, and few-shot prompting
  • System prompts vs user prompts
  • Role-based prompting and persona design
  • CRAFT and RISE frameworks
  • Bias detection and responsible prompting
Hands-On
  • Design prompts for 5 different business scenarios
  • Compare zero-shot vs few-shot on the same task
  • Build an AI Blog Generator using structured prompts
Sunday · Day 2
Move from basic prompting to building persistent, customized AI tools without writing code. Learn how to package system instructions, specialized knowledge bases, and specific capabilities into custom GPTs or Google Gems that solve repeatable enterprise tasks.
Topics Covered
  • Introduction to no-code AI customization
  • Anatomy of a Custom GPT and Google Gem
  • Writing effective system instructions for custom agents
  • Configuring knowledge retrieval (uploading enterprise files)
  • Actions and API integration basics for no-code environments
  • Business use cases for custom micro-agents
Hands-On
  • Build a custom GPT or Google Gem for a business use case
  • Upload a custom knowledge base document and test retrieval
  • Create a Customer Support Assistant or Technical Code Reviewer
Saturday · Day 3
Explore how Chain-of-Thought prompting improves reasoning in complex, multi-step tasks. Understand the limits of model context windows and learn how to iteratively evaluate prompt performance to ensure reliable, structured outputs.
Topics Covered
  • Chain-of-Thought (CoT) and Tree-of-Thought prompting
  • Context window management, token budgets, and truncation
  • Mitigating model hallucinations
  • Prompt evaluation and iteration techniques
  • Model benchmarking: ChatGPT, Gemini, Claude, Mistral
Hands-On
  • Build a complex Chain-of-Thought prompt for a business problem
  • Conduct a multi-model evaluation exercise
  • Analyze how different models handle identical instruction sets
Sunday · Day 4
Understand what makes an autonomous AI agent fundamentally different from a simple conversational chatbot. Explore the theoretical journey of AI from rule-based logic to modern agentic loops and study the ReAct pattern where models autonomously reason about goals and select tools to achieve them.
Topics Covered
  • What is an AI Agent? Agent vs chatbot vs autonomous workflow
  • The ReAct agent pattern: Reason + Act
  • Evolution of AI: Rule-based → ML → DL → Transformers → LLMs → Agents
  • Real-world applications of autonomous agent workflows
  • Introduction to tool-calling and function execution
Hands-On
  • Map out a complex business workflow into separate agent roles
  • Mock-test a manual ReAct loop using a standard LLM interface
  • Simulate tool execution with Planner, Researcher, Writer roles
Saturday · Day 5
Set up a professional Python development environment from scratch. Learn the difference between IDEs, notebooks, and the terminal, and understand when to use each. Build a solid foundation in Python syntax including variables, loops, conditionals, and functions to prepare for writing programmatic AI applications.
Topics Covered
  • What is an IDE? VS Code, PyCharm, Jupyter Notebook
  • Python installation and virtual environments
  • pip package manager and library ecosystem
  • Python basics: variables, data types, control flow
  • Functions, modules, and file I/O
  • Python library vs framework vs module
Hands-On
  • Install VS Code and configure the Python extension
  • Create and activate a virtual environment
  • Write your first functional Python script
Sunday · Day 6
Get hands-on with NumPy, the backbone of numerical computing in Python. Master Pandas, Python's most powerful data manipulation library, to load, inspect, filter, and aggregate real-world datasets—a critical prerequisite for feeding unstructured data into vector databases and RAG systems.
Topics Covered
  • Introduction to NumPy: arrays and operations
  • NumPy broadcasting and mathematical functions
  • Introduction to Pandas: Series and DataFrames
  • Data loading: CSV, Excel, JSON
  • Data exploration: head, info, describe, dtypes
  • Filtering, sorting, and grouping data
  • Handling missing values and data cleaning
Hands-On
  • NumPy array operations and mathematical functions exercise
  • Load, clean, filter, and aggregate a sales dataset using Pandas
  • Answer analytical questions from real-world data
Saturday · Day 7
Understand how text is converted into numerical vectors (embeddings) that capture semantic meaning. Learn the difference between dense and sparse representations and why vector databases are essential for AI applications. Explore leading vector databases including ChromaDB and FAISS, practicing document storage, embedding generation, and metadata filtering.
Topics Covered
  • What are embeddings? Word2Vec, GloVe, Sentence Transformers
  • Dense vs sparse vectors
  • Introduction to vector databases: Chroma, FAISS, Pinecone
  • Similarity search: cosine similarity, dot product
  • Storing and retrieving documents using vector DB
  • Chunking strategies: fixed size, recursive, semantic
  • Metadata filtering in vector stores
Hands-On
  • Generate embeddings using OpenAI text-embedding-3-small
  • Set up ChromaDB locally
  • Store 50 documents and run similarity search
  • Metadata filtering exercise
  • Compare chunking strategies on a long document
Sunday · Day 8
Understand why Retrieval Augmented Generation (RAG) was created and how it solves the hallucination and knowledge cutoff problems of LLMs. Study the complete RAG pipeline from document ingestion through retrieval to answer generation. Build a working RAG pipeline using both LangChain and LlamaIndex.
Topics Covered
  • Why RAG? Solving LLM hallucination and knowledge cutoff
  • RAG architecture: Indexing → Retrieval → Generation
  • Document loaders and text splitters
  • Retrieval strategies: similarity search, MMR
  • Combining retrieved context with LLM prompts
  • Naive RAG vs Advanced RAG overview
  • Introduction to LlamaIndex as an alternative to LangChain
Hands-On
  • Build a basic RAG pipeline with LangChain + ChromaDB
  • Load a PDF and answer questions from it
  • Experiment with chunk size and overlap
  • Replicate the same pipeline with LlamaIndex
Saturday · Day 9
Go beyond prompt engineering to context engineering: learn how to manage what information enters the LLM context window strategically. Understand token budgets, truncation strategies, and how to handle long conversation histories. Build a production-grade FastAPI backend that exposes your RAG pipeline as a REST API.
Topics Covered
  • What is Context Engineering? Beyond prompt engineering
  • Context window management: token budgets and truncation
  • Conversation history management in production
  • Building a FastAPI backend for a RAG application
  • REST API design: /query, /upload, /health endpoints
  • Session management and multi-turn conversation context
  • Error handling and logging in AI applications
Hands-On
  • Build a FastAPI backend with LangChain RAG pipeline
  • Create /upload endpoint to ingest documents
  • Build /query endpoint with context-aware responses
  • Test all endpoints with Postman or curl
Sunday · Day 10
Build a clean, interactive chat UI using Streamlit. Learn to use Streamlit's chat elements, file uploader, and session state to create a responsive document Q&A interface. Connect your Streamlit frontend to the FastAPI backend, deploy the complete application, and manage secrets securely.
Topics Covered
  • Introduction to Streamlit: building data apps fast
  • Streamlit components: text input, file upload, chat elements
  • Connecting Streamlit frontend to FastAPI backend
  • State management in Streamlit (st.session_state)
  • Deploying to Streamlit Cloud
  • Environment variables and secrets management
  • End-to-end testing of a full RAG application
Hands-On
  • Build a document Q&A chat interface in Streamlit
  • Connect frontend to FastAPI backend
  • Add file upload and conversation history
  • Deploy complete app to Streamlit Cloud
Saturday · Day 11
Understand why naive RAG fails in production and learn the advanced techniques that address poor retrieval quality. Study BM25 sparse retrieval, hybrid search, and how combining dense and sparse approaches improves recall. Learn how reranking models act as a second-pass filter to dramatically improve the relevance of retrieved documents.
Topics Covered
  • Limitations of naive RAG: retrieval quality problems
  • BM25 sparse retrieval: TF-IDF principles
  • Hybrid search: combining dense + sparse retrieval
  • Reranking with Cohere Rerank and cross-encoders
  • Multi-query retrieval and query expansion
  • Parent-child document retrieval
  • Contextual compression and LLM-based extractors
  • Evaluation metrics: MRR, NDCG, Precision@K
Hands-On
  • Implement BM25 retrieval with rank_bm25
  • Build a hybrid search pipeline (BM25 + vector)
  • Add Cohere Reranker to your RAG pipeline
  • Compare naive vs advanced RAG retrieval quality
Sunday · Day 12
Learn how knowledge graphs capture structured relationships between entities and how GraphRAG combines graph traversal with vector search for richer retrieval. Set up Neo4j and write basic Cypher queries. Build a command-line RAG tool that integrates knowledge graph and vector retrieval strategies using argparse and rich.
Topics Covered
  • Introduction to Knowledge Graphs: nodes, edges, relationships
  • GraphRAG: combining knowledge graphs with vector retrieval
  • Neo4j basics and Cypher query language
  • Building a graph-based retriever with LangChain
  • CLI tool architecture using argparse and rich library
  • Combining Knowledge Graph + Vector Search in one CLI tool
  • Advanced RAG CLI: multi-strategy retrieval
Hands-On
  • Set up Neo4j and load sample knowledge graph data
  • Write Cypher queries to traverse the graph
  • Build a CLI RAG tool with argparse
  • Combine knowledge graph + vector search in one query pipeline
Saturday · Day 13
Understand the architecture of multi-agent systems and why distributing work across specialized agents produces better results than a single generalist agent. Learn CrewAI's core concepts: Crew, Agent, Task, and Process. Design agent roles with clear responsibilities and learn how CrewAI orchestrates their collaboration.
Topics Covered
  • What are Multi-Agent Systems (MAS)?
  • Agent roles: Planner, Researcher, Writer, Critic
  • Agent communication and task delegation patterns
  • Introduction to CrewAI: Crew, Agent, Task, Process
  • Sequential vs hierarchical vs collaborative processes
  • Tool integration in CrewAI agents
  • LangGraph vs CrewAI: when to use which
Hands-On
  • Install CrewAI and configure API keys
  • Build a 3-agent research crew (Researcher, Analyst, Writer)
  • Add web search tool to your agents
  • Run a sequential content generation pipeline
Sunday · Day 14
Build advanced CrewAI workflows with a manager agent that delegates tasks hierarchically. Add memory systems so agents retain knowledge across runs, and create custom tools that extend agent capabilities. Implement human-in-the-loop checkpoints, validate structured JSON output, and deploy your crew as a FastAPI endpoint.
Topics Covered
  • Hierarchical process with manager agent
  • Memory systems in CrewAI: short-term, long-term, entity
  • Custom tool creation for CrewAI agents
  • Async agents and parallel task execution
  • Human-in-the-loop with CrewAI
  • Structured output schemas and output callbacks
  • Deploying a multi-agent pipeline with FastAPI
Hands-On
  • Build a full blog research-to-publish multi-agent system
  • Implement manager agent for task delegation
  • Add structured JSON output validation with Pydantic
  • Deploy a CrewAI pipeline with FastAPI
Saturday · Day 15
Understand the Claude model family and how to use the Anthropic API with messages, system prompts, and tool use. Install and explore Claude Code, Anthropic's agentic coding tool. Learn how CLAUDE.md files give Claude persistent project context, how hooks customize Claude's behavior, and how to create reusable Skills for common tasks.
Topics Covered
  • Claude model family: Haiku, Sonnet, Opus
  • Claude API: messages, system prompts, tools and tool use
  • What makes Claude different: Constitutional AI design
  • Introduction to Claude Code: agentic coding in the terminal
  • CLAUDE.md: defining project context and coding standards
  • Hooks in Claude Code: pre and post tool hooks
  • Skills in Claude: what they are and how to create custom skills
  • Context window management in Claude Code
Hands-On
  • Set up Claude API and make your first API call
  • Install Claude Code and explore commands
  • Write a CLAUDE.md for an existing project
  • Create a custom Skill for a repetitive code task
  • Use Claude Code to build a small utility autonomously
Sunday · Day 16
Learn the Model Context Protocol (MCP), the open standard that lets Claude connect to external tools, databases, and services. Dive deep into Harness Engineering, an essential production practice where code generated by AI agents is automatically executed and validated inside an isolated test harness. Learn how to configure automated feedback loops where execution failures or test errors are programmatically fed back into the agent, allowing the system to self-correct its bugs without human intervention.
Topics Covered
  • What is MCP (Model Context Protocol)?
  • MCP architecture: Host, Client, Server
  • Built-in MCP servers: filesystem, browser, web search
  • Building a custom MCP server
  • Connecting Claude to external tools via MCP
  • Introduction to Harness Engineering
  • Designing automated test harnesses for AI code generation
  • Auto-correct loops: capturing test errors for automatic self-healing
  • Safety and responsible use of Claude in production
Hands-On
  • Set up an MCP server with filesystem access
  • Connect Claude to a custom MCP tool
  • Build an automated Python test harness for Claude Code
  • Program an auto-correct script for self-healing code
Saturday · Day 17
Learn why production AI systems need guardrails to prevent harmful outputs, PII leakage, and prompt injection attacks. Implement input and output validators using Guardrails AI and NeMo Guardrails. Evaluate your RAG pipeline end-to-end using RAGAS and LangSmith, understanding key metrics like faithfulness, answer relevance, and context precision.
Topics Covered
  • Why AI safety matters in production systems
  • Input and output guardrails: PII detection, toxicity filtering
  • Prompt injection attacks and defences
  • Introduction to Guardrails AI and NeMo Guardrails
  • Responsible AI principles: fairness, accountability, transparency
  • LLM evaluation frameworks: RAGAS, LangSmith, TruLens
  • Evaluation metrics: faithfulness, relevance, context precision
  • A/B testing LLM outputs and monitoring in production
Hands-On
  • Set up Guardrails AI with a custom validation schema
  • Detect and block PII in LLM outputs
  • Evaluate a RAG pipeline with RAGAS
  • Build an LLM evaluation dashboard with LangSmith
Sunday · Day 18
Understand why organizations run models locally for privacy, cost reduction, and low latency. Install Ollama, download open-source models including Llama 3 and Mistral, and integrate them into your LangChain pipelines. Learn the fundamentals of parameter-efficient fine-tuning (LoRA/QLoRA), prepare a training dataset, and fine-tune a model using Unsloth.
Topics Covered
  • Why run models locally? Privacy, cost, and latency
  • Introduction to Ollama: downloading and running local LLMs
  • Local model families: Llama 3, Mistral, Phi-3, Gemma
  • Knowledge distillation: teacher-student model concept
  • Fine-tuning LLMs: full fine-tune vs LoRA vs QLoRA
  • Preparing a fine-tuning dataset
  • Fine-tuning with Hugging Face PEFT + TRL and Unsloth
  • Evaluating a fine-tuned model vs base model
Hands-On
  • Download and run Llama 3 locally with Ollama
  • Integrate local model with LangChain
  • Prepare a custom fine-tuning dataset
  • Fine-tune a small model with QLoRA using Unsloth
  • Compare base vs fine-tuned model responses

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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