Generative AI

₹45,000/-

Generative AI Course near Kurnool, 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

1 Jun 2026Next Batch

100%Placement Support

About

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

"Generative AI is no longer an experimental technology — it is actively reshaping how businesses build products, automate workflows, and deliver value to customers."

Companies across every sector are hiring professionals who can do more than just use AI tools: they need engineers and developers who understand how large language models work, how to build reliable pipelines on top of them, and how to deploy production-ready AI systems. The IntellentX Generative AI course is built for exactly this purpose. This is a comprehensive, 3-month, project-based programme that takes you from Python and NLP fundamentals all the way through to building and deploying full-scale AI applications. The curriculum is structured around real application development — not theory slides. From week one, you work with tools and frameworks that are standard in the industry: LangChain and LangGraph for orchestration, Hugging Face and OpenAI for model access, FAISS and AstraDB for vector storage, and Streamlit for rapid application deployment. You learn how transformers work, how to engineer prompts effectively, how to build Retrieval Augmented Generation pipelines, and how to design autonomous multi-agent systems using CrewAI. The projects in this course reflect the kinds of systems that companies are building and hiring for right now. You will build document-based question answering assistants, AI-powered chatbots with memory and context management, SQL agents that translate natural language into database queries, multi-agent research and automation workflows, and fine-tuned language models deployed on cloud platforms. By the time you complete the programme, you will have a portfolio of 10+ production-grade GenAI projects that demonstrate your ability to design, build, and ship AI systems end to end. One of the most common misconceptions about Generative AI is that it is only for researchers or data scientists with advanced mathematics backgrounds. This course is designed to change that. If you have basic Python knowledge and an understanding of how machine learning works, you have everything you need to start. The programme is structured to build your understanding progressively — starting with the foundations of NLP and transformer architecture, and moving through increasingly complex systems including RAG, agents, fine-tuning, and multi-agent orchestration. Career outcomes for Generative AI specialists are strong and growing. Roles such as Generative AI Engineer, Prompt Engineer, LLM Application Developer, AI Product Engineer, and MLOps / LLMOps Engineer are in active demand at product companies, AI startups, and large enterprises. IntellentX's placement cell provides dedicated support throughout your job search — including resume and portfolio review, technical mock interviews focused on LLM systems and AI architecture, and direct referrals to our network of 50+ hiring partners. Both online and offline batches are available, with flexible timings designed to accommodate working professionals and full-time students alike.

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 Kurnool?

Why students from Kurnool choose IntellentX for Generative AI

Kurnool's engineering graduates are ambitious and academically strong — but quality Generative AI training locally is hard to find. IntellentX's live online Generative AI programme brings Hyderabad-standard instruction directly to Kurnool students: the same 3 Months curriculum, the same industry-practitioner trainers, and the same hands-on project work that our campus students complete. Batch sizes are capped at 20 to ensure personalised attention and direct trainer interaction throughout the course. Our placement cell has successfully connected Andhra Pradesh students with Generative AI roles at IT companies across Hyderabad, Bengaluru, Chennai, and Pune — and that support is fully available to every enrolled student from Kurnool, regardless of location.

FAQs — Generative AI near Kurnool

Common questions from students near Kurnool

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