Generative AI

₹45,000/-

Generative AI Course near Madhapur, Hyderabad

Generative AI

Learn online from anywhere or attend offline classes in Hyderabad while mastering practical Generative AI development with LangChain, Hugging Face, vector databases, RAG pipelines, autonomous agents, and production-ready project work.

3 MonthsDuration

Online / OfflineMode

4.8 ★Rating

18 May 2026Next Batch

100%Placement Support

About

What this program is about and what you'll achieve.

"This course is a comprehensive, project-based program designed to help learners master the development of end-to-end Generative AI applications using LangChain, Hugging Face, and Vector Databases."

Delivered through online and offline training in Hyderabad, it covers the full journey from Python and NLP foundations to transformers, RAG pipelines, autonomous agents, multi-agent orchestration, and production deployment. Learners build practical applications such as chatbots, document assistants, agentic search systems, SQL agents, summarizers, and cloud-scale GenAI workflows. By the end of the program, students are prepared to build intelligent AI applications for business, product, and automation use cases with confidence.

Tools & Stack

Technologies and platforms you'll work with hands-on.

LangChain and LangGraph

Hugging Face and OpenAI/Groq

AstraDB and FAISS (Vector Databases)

Streamlit and Python

RAG, multi-agent systems (CrewAI), and model fine-tuning workflows

Projects

Real-world projects you'll build to strengthen your portfolio.

01PROJECT 01

End-to-End Q&A Application with OpenAI/Hugging Face

A foundational project covering the complete lifecycle of a Generative AI app — connecting prompt templates to LLMs and deploying the interface using Streamlit..

02PROJECT 02

Conversational Q&A Chatbot with Message History

A chatbot that maintains context across multiple turns using LangChain's memory components, ensuring the model remembers previous user inputs for a natural conversational flow..

03PROJECT 03

RAG Document Q&A

A high-performance RAG pipeline using the Groq inference engine for lightning-fast question-answering over custom documents, leveraging the speed of Llama 3..

04PROJECT 04

Conversational Chat with PDF System

A specialized RAG application that allows users to upload multiple PDF files and ask questions, combining document loaders, text splitters, and vector storage to provide accurate citations from source text..

05PROJECT 05

PDF Query RAG with AstraDB

A production-ready RAG system using AstraDB as a cloud-native vector database, focusing on scalable data retrieval beyond local vector stores..

06PROJECT 06

Search Engine with LangChain Tools and Agents

An autonomous agent that browses the web to answer real-time queries, integrating tools like Arxiv, Wikipedia, and DuckDuckGo to provide answers beyond the model's training data..

07PROJECT 07

Chat with SQL Databases

Uses the LangChain SQL Toolkit to enable natural language querying of structured data — the agent translates user questions into SQL queries, executes them, and summarizes the results..

08PROJECT 08

Text-to-Math Problem Solver using Google Gemma 2

An assistant built with the Gemma 2 model and specialized math tools to solve complex word problems, highlighting how to constrain LLMs to specific reasoning tasks and calculation tools..

09PROJECT 09

Multi-Strategy Text Summarization

Explores different summarization architectures including Stuff, Map-Reduce, and Refine chains — covering which strategy best suits documents of varying lengths and complexities..

10PROJECT 10

YouTube Video and Website URL Summarizer

A practical tool that extracts transcriptions from YouTube videos or text from URLs to generate concise summaries, handling unstructured web data end-to-end..

11PROJECT 11

Stateful Multi-Actor Applications with LangGraph

Introduces agentic workflows where multiple actors collaborate to solve a task, covering state management and persistence in complex, non-linear AI applications..

12PROJECT 12

End-to-End LLM Fine-Tuning with Lamini

A deep dive into model optimization — fine-tuning a large language model on a specific dataset, covering training data preparation and technical execution to improve performance on specialized tasks..

Career Outcomes

Roles and opportunities you'll be ready for after completing this program.

Generative AI Developer

AI Solutions Engineer

NLP Specialist

AI Automation Engineer

LLM Data Scientist

LLM Engineer

Curriculum

10 modules covering everything from fundamentals to advanced topics.

Foundational Python: Control Flow, Data Structures (Lists, Dictionaries), and Functions

Modular Programming: Importing Packages, Modules, and File Handling

OOP Concepts: Classes, Objects, and Exception Handling

Streamlit: Building Web Interfaces for AI Applications

Machine Learning for NLP: Foundational concepts and text preprocessing

Recurrent Neural Networks (RNNs): Simple RNN, LSTM (Long Short-Term Memory), and GRUs

Sequence Models: Bidirectional RNNs and Encoder-Decoder architectures

Attention Mechanism: Intuition and implementation of Seq2Seq networks

Transformer Architecture: Multi-head Attention, Positional Encoding, and Feed-forward networks

LLM Basics: Introduction to Generative AI and Large Language Models

Hugging Face Ecosystem: Integrating pre-trained models and using the Hugging Face Hub

Introduction to LangChain: Framework overview and setup with OpenAI/Ollama

LangChain Components: Prompts, Models, Output Parsers, and Memory

LCEL (LangChain Expression Language): Building basic LLM applications and complex chains

Chatbots: Building conversational interfaces with message history

RAG Pipeline: Document Loading, Text Splitting, and Embeddings

Vector Databases: Practical implementation with AstraDB and FAISS

Advanced RAG: Hybrid Search, PDF Querying, and Document Q&A using Groq API and Llama 3

Graph RAG: Introduction to Graph Databases and Cypher Query Language with LangChain

LangChain Agents: Using search engines and external tools

SQL Toolkit: Chatting with SQL Databases using LangChain Agents

Autonomous Tools: Creating a Text-to-Math problem solver (using Google Gemma 2)

Summarization: Text summarization for YouTube videos and Website URLs

Coding Assistants: Building Multi-language Code Assistants using CodeLlama

Specialized Models: Working with Google Gemini, Nvidia NIM, and open-source models

Fine-tuning Intuition: Understanding when and why to fine-tune

Practical Fine-tuning: End-to-end model fine-tuning implementation using the Lamini platform

CrewAI: Creating Multi-AI Agent systems for real-world automated workflows

LangGraph: Building stateful, multi-actor applications for complex, non-linear AI workflows

Streamlit & HuggingFace Spaces: Deploying GenAI apps for public access

AWS Integration: Overview of Generative AI services on AWS

Cloud Deployment: Best practices for scaling and monitoring LLM applications

Prerequisites

What you should know or have before joining this program.

Decent proficiency in Python programming and text/data manipulation using NumPy and Pandas

Working knowledge of classical machine learning models and their logic

Conceptual understanding of deep learning fundamentals such as ANNs and CNNs

Who Is This For?

This program is designed for the following learners.

Data professionals and ML engineers transitioning into Generative AI

Software developers and AI builders creating production-ready AI applications

AI researchers and educators seeking a structured, project-oriented GenAI curriculum

Why study near Madhapur?

Why students from Madhapur choose IntellentX for Generative AI

Madhapur sits at the heart of Hyderabad's technology economy, and professionals here face constant pressure to upskill. IntellentX's Generative AI programme is a practical, 3 Months course built around what companies in Madhapur, Kondapur, and Gachibowli actually hire for. Whether you are transitioning into Generative AI from a different tech role or building on an existing foundation, our project-driven curriculum and small batch sizes give you the depth of learning that self-study and online courses cannot replicate. Our trainers have held actual Generative AI positions at reputed firms — so the curriculum reflects real workplace expectations, not just exam preparation. Evening and weekend batches make it easy to train without disrupting your current job.

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