Generative AI is reshaping how businesses create content, automate workflows, and build intelligent products, and kodestree’s Generative AI Course helps you keep pace with this shift. Across 35 hours of live, instructor-led sessions, you move from Core AI and Machine Learning Concepts to advanced topics such as Large Language Models, Prompt Engineering, Retrieval-Augmented Generation, AI Agents, and Enterprise deployment. The training blends conceptual clarity with hands-on labs, so you build real applications instead of only watching demonstrations. Whether a fresher, working professional, or business leader, this course prepares you for practical, in-demand Generative AI roles.
Prerequisites
- No prior AI or Machine Learning background is required to start this course.
- Basic computer literacy and comfort using the internet.
- Familiarity with any programming language is helpful but not mandatory.
- Basic logical and analytical thinking to follow AI workflows more easily.
- A genuine interest in understanding how AI tools generate text, images, code, and other content.
Why Learn Generative AI in 2026
Generative AI has moved from an experimental technology to a core part of how enterprises build products, serve customers, and make decisions. In 2026, organizations across banking, retail, healthcare, and technology are actively hiring professionals who can work with Large Language Models, design reliable prompts, build Retrieval-Augmented Generation pipelines, and deploy AI agents that reason and act with minimal supervision. Industry reports continue to show strong year-on-year growth in AI hiring, and Generative AI-specific skills command a noticeable salary premium over generalist AI or software roles. At the same time, tools such as ChatGPT, Claude, and Gemini have become everyday utilities inside marketing, engineering, and operations teams, making Generative AI literacy relevant for technical and non-technical professionals alike. Learning it now means getting ahead of a curve that is still rising, rather than catching up to one that has already flattened out.
Course Objectives
By the end of this course, you will be able to design, build, and deploy real-world Generative AI applications with confidence. This program is built to help you:
- Understand the Core Concepts of Generative AI, Large Language Models, and Transformer architecture
- Write and optimize prompts for tools like ChatGPT, Claude, and Gemini
- Build Retrieval-Augmented Generation (RAG) pipelines using vector databases
- Develop AI agents and multi-step automation workflows
- Integrate Generative AI APIs into real applications using Python
- Apply responsible AI, safety, and governance practices while building solutions
- Deploy and evaluate Generative AI models for production use cases
What You Will Learn
This course takes you step by step from AI fundamentals to advanced, job-ready Generative AI skills, including:
- Foundations of Artificial Intelligence, Machine Learning, and Deep Learning
- Large Language Model architecture and how models such as GPT and LLaMA are trained
- Prompt engineering techniques, from zero-shot to chain-of-thought prompting
- Retrieval-Augmented Generation, embeddings, and vector databases
- Building AI chatbots, content generators, and automation tools
- LangChain and LangGraph for application development
- Fine-tuning LLMs using techniques such as LoRA and QLoRA
- Agentic AI, LangGraph, CrewAI and Autonomous Agents
- Building and orchestrating AI agents
- Image, audio, and video generation using modern AI models
- Deploying Generative AI applications and optimizing them for cost and performance
Who is This Course For?
This Generative AI course is designed for a wide range of learners, including:
- Absolute beginners looking to start a career in AI
- Software developers and engineers wanting to add Generative AI skills
- Data analysts, data engineers, and data scientists
- Business analysts and product managers
- Marketing and content professionals exploring AI-driven workflows
- IT professionals and automation specialists
- Students and recent graduates planning an AI career
- Entrepreneurs and startup founders building AI-powered products
- Corporate teams undergoing AI transformation
Top 5+ Tools You Will Work With
- ChatGPT and OpenAI API
- Google Gemini
- Anthropic Claude
- Meta Llama
- Hugging Face
- LangChain and LangGraph
- LlamaIndex
- Python and Jupyter Notebook
- Vector Databases (ChromaDB, FAISS, Milvus)
- Git and GitHub
Skills You Will Gain
By the time you complete this training, you will have hands-on command over:
- Prompt engineering and prompt optimization
- LLM application development
- Retrieval-Augmented Generation (RAG) design
- AI agent building and orchestration
- Model fine-tuning and evaluation
- API integration and workflow automation
- Vector database implementation
- AI ethics, safety, and governance awareness
Projects You Will Work On
1. AI-Powered Content Generator
Build a simple application that uses the OpenAI or Gemini API to generate blog posts, product descriptions, or social media captions based on user prompts. You’ll practice prompt engineering techniques, handle API responses in Python, and add basic customization options like tone and length.
2. Document Q&A Chatbot using RAG
Create a chatbot that can answer questions from a set of PDFs or text documents. This project walks you through generating embeddings, storing them in a vector database (like ChromaDB or FAISS), and retrieving relevant context to feed into an LLM for accurate, grounded responses.
3. Autonomous Task-Automation Agent
Design a simple AI agent using LangChain or LangGraph that can plan and execute a multi-step task for example, researching a topic online, summarizing findings, and drafting an email or report. This project introduces agent orchestration, tool-calling, and basic workflow automation.
Career Outcomes
Completing this course opens doors to a wide range of in-demand Generative AI careers, such as:
- Generative AI Engineer
- Prompt Engineer
- AI Application Developer
- LLM Integration Specialist
- AI Automation Engineer
- Conversational AI Developer
- AI Product Specialist
- AI Research Assistant
- AI Consultant
Generative AI Professionals Salary
Generative AI professionals are in growing demand across technology, consulting, finance, healthcare, and other industries. Salaries vary based on experience, location, technical skills, job role, and organization. The following table provides salary benchmarks for Generative AI-related roles in India and the USA.
| Generative AI Job Role | India – Average/Typical Salary | USA – Average/Typical Salary |
|---|---|---|
| Generative AI Engineer | ₹9 LPA base pay | $152,868/year |
| AI/ML Engineer | ₹6-₹25 LPA | $152,868/year |
| AI Developer | ₹8-₹20 LPA | $152,462/year |
| Senior Generative AI / AI Engineer | ₹14-₹30+ LPA | $180,000-$250,000+* |
Note: Salary figures are based on available market data from Glassdoor and Indeed. Actual compensation can vary based on experience, location, employer, technical expertise, bonuses, and equity.
Top Hiring Companies
- Microsoft
- Amazon
- Meta
- IBM
- NVIDIA
- Accenture
- TCS
- Infosys
- Wipro
- Cognizant
- Capgemini
- Tech Mahindra
- Deloitte
Why Choose kodestree for This Training?
kodestree has trained thousands of professionals across AI and emerging technologies, and here is what makes this Generative AI course worth your time:
- Live, instructor-led training delivered by trainers with real industry experience
- 100% hands-on approach with real Generative AI projects, not just theory
- Small batch sizes for better interaction and doubt resolution
- Flexible weekday, weekend, and fast-track batch options
- Lifetime access to recorded sessions and course materials
- Course completion certificate recognized by hiring partners
- 24×7 learner support and post-training career assistance
- Corporate training options for teams adopting Generative AI
Generative AI Trainer
Ravi Singh
Ex-Fortune 500 AI Team
Ravi is a software engineer and AI workflow consultant with over 15 years of experience in full-stack development, Software Development, Machine Learning, and applied Generative AI. Over the last few years, he has worked extensively with Large Language Models and modern AI frameworks, helping engineering and product teams at startups and enterprises design, fine-tune, and deploy Generative AI applications, from prompt engineering and RAG pipelines to AI agent orchestration and production-grade model deployment.
Expertise:
- Large Language Models (LLMs) & Transformer architecture
- Prompt engineering & Retrieval-Augmented Generation (RAG)
- LangChain, LangGraph & vector database integration
- LLM fine-tuning (LoRA, QLoRA) & model evaluation
- Python, AI agent development, and Generative AI application deployment
Highlights:
- 15+ years of industry experience in software engineering and applied AI
- Hands-on experience building and deploying Generative AI applications in production environments
- Trained professionals from companies across IT, fintech, and product engineering
- Known for breaking down complex Generative AI concepts into practical, job-ready skills
