Generative AI & AI Tools Course in Jaipur
Learn to use and build Generative AI applications, from language-model concepts and prompt design to retrieval, assistants and agent workflows. Work with Python and APIs, evaluate outputs against a clear task and consider privacy, reliability and responsible use.
- Practical learning
- Project practice
- Jaipur
Generative AI & AI Tools
Build your foundation in Generative AI & AI Tools
Learn to use and build Generative AI applications, from language-model concepts and prompt design to retrieval, assistants and agent workflows. Work with Python and APIs, evaluate outputs against a clear task and consider privacy, reliability and responsible use.
Begin with model concepts, task definition and prompt design, then use Python and APIs to connect Generative AI to an application. Study tokens, context windows and transformer ideas before moving to structured outputs and multi-step workflows.
Application topics include content and generated media, embeddings, vector search, Retrieval Augmented Generation, chatbots and tools that an agent can call. Projects should record how outputs are evaluated, where source material is used and when human review is necessary.
This course focuses on LLM and Generative AI applications. The Artificial Intelligence course covers broader AI systems, while Data Science and Machine Learning emphasize data analysis and predictive modelling. AI Productivity Tools provides a related path for everyday workplace tasks.
Who can join?
Beginners can start with AI tools and prompting. Learners who want to build applications should add Python and API foundations.
Skills and tools
Course curriculum
Explore the published learning outline below. Confirm the detailed syllabus and module coverage for your chosen batch with the course team.
Introduction to Generative AI
Identify suitable uses for Generative AI and understand its model types, tools, benefits and limitations.
- Introduction to Generative AI
- How Generative AI Works
- Generative AI vs Traditional AI
- Types of Generative AI Models
- Real-World Applications of Generative AI
- Generative AI Industry and Career Opportunities
- Understanding Generative AI Use Cases
- Generative AI Tools and Ecosystem
- Benefits and Limitations of Generative AI
- Setting Up Your Generative AI Learning Environment
AI and Machine Learning Fundamentals
Review datasets, features, labels, neural-network basics and evaluation as foundations for understanding generative systems.
- Introduction to Artificial Intelligence
- Introduction to Machine Learning
- Supervised and Unsupervised Learning Overview
- Introduction to Deep Learning
- Artificial Neural Networks Basics
- Training AI Models
- Understanding Datasets
- Features and Labels
- Model Evaluation Concepts
- AI and Machine Learning Fundamentals Practice
Python for Generative AI
Use Python, JSON and APIs to build small application workflows and manage configuration without exposing credentials.
- Introduction to Python for Generative AI
- Python Variables and Data Types
- Conditional Statements and Loops
- Functions and Modules
- Lists and Dictionaries
- Working with APIs in Python
- JSON Data Handling
- Environment Variables and API Keys
- Python Libraries for AI
- Python Mini Project
Large Language Models
Understand tokens, context windows, training, fine-tuning, inference and differences between model access approaches.
- Introduction to Large Language Models
- How LLMs Work
- Training Data and Tokens
- Understanding Context Windows
- Pre-Training Concepts
- Fine-Tuning Concepts
- Instruction Following
- Inference and Model Outputs
- Open-Source vs Closed LLMs
- Working with LLM Applications
Transformer Architecture
Explore attention, encoder and decoder concepts and how transformer models process sequences.
- Introduction to Transformers
- Understanding Attention Mechanisms
- Self-Attention Concepts
- Encoder Architecture
- Decoder Architecture
- Encoder-Decoder Models
- Positional Encoding
- Transformer-Based Language Models
- Applications of Transformers
- Transformer Concepts Practice
Prompt Engineering Fundamentals
Specify tasks, context, examples and constraints, then test prompt quality and reliability.
- Introduction to Prompt Engineering
- Understanding Prompts and Context
- Zero-Shot Prompting
- One-Shot Prompting
- Few-Shot Prompting
- Role and System Instructions
- Prompt Templates
- Chain-of-Thought Concepts
- Prompt Evaluation
- Prompt Engineering Best Practices
Advanced Prompt Engineering
Design structured outputs, extraction and multi-step prompts, and improve them through testing and iteration.
- Prompt Optimization Techniques
- Structured Output Prompts
- JSON and Data Extraction Prompts
- Content Generation Prompts
- Research and Analysis Prompts
- Code Generation Prompts
- Image Generation Prompts
- Multi-Step Prompt Workflows
- Prompt Testing and Iteration
- Advanced Prompt Engineering Project
Generative AI for Content
Build text and content workflows for drafting, summarizing, editing, translation and communication tasks.
- AI Text Generation Fundamentals
- Blog and Article Generation
- Social Media Content Generation
- Marketing Copy Generation
- Email Content Generation
- Content Summarization
- Content Rewriting and Editing
- Translation and Localization Concepts
- Building AI Content Workflows
- AI Content Generation Project
Image and Video Generative AI
Explore text-to-image and text-to-video workflows, visual composition, editing and responsible generated-media use.
- Introduction to AI Image Generation
- Understanding Text-to-Image Models
- Writing Effective Image Prompts
- Image Styles and Composition
- Image Editing Concepts
- Introduction to AI Video Generation
- Text-to-Video Concepts
- AI Video Workflow Concepts
- Responsible Use of Generated Media
- Generative Media Project
Embeddings and Vector Databases
Create and store embeddings, prepare document chunks and retrieve information through similarity search.
- Introduction to Embeddings
- Understanding Semantic Search
- Creating Text Embeddings
- Similarity Search Concepts
- Introduction to Vector Databases
- Storing and Retrieving Embeddings
- Document Chunking Strategies
- Metadata and Filtering Concepts
- Vector Search Applications
- Embeddings and Search Project
Retrieval Augmented Generation
Combine document processing, retrieval and language models, and evaluate answers against source material.
- Introduction to RAG
- How RAG Systems Work
- Document Loading and Processing
- Text Chunking
- Embedding Documents
- Vector Retrieval
- Combining Retrieval with LLMs
- Context Management
- RAG Evaluation Concepts
- Building a RAG Application
AI Chatbots and Assistants
Design assistants around user input, context and knowledge sources, then test conversational behaviour and limitations.
- Introduction to AI Chatbots
- Designing Conversational Experiences
- User Input and Context Handling
- Conversation Memory Concepts
- Building a Knowledge Assistant
- Document-Based Chatbots
- Customer Support Chatbot Concepts
- Multilingual Assistant Concepts
- Chatbot Testing
- AI Chatbot Project
AI Agents and Automation
Explore tool calling, memory, API integration and multi-step agent workflows for a defined task.
- Introduction to AI Agents
- Agent Architecture Concepts
- Tools and Function Calling
- Planning and Reasoning Concepts
- Multi-Step Agent Workflows
- AI Agent Memory Concepts
- Connecting Agents with APIs
- AI Workflow Automation
- Multi-Agent System Concepts
- AI Agent Project
Generative AI Application Development and Deployment
Plan a small application, connect model APIs and a user interface, and test configuration, deployment and monitoring.
- Planning a Generative AI Application
- Working with Generative AI APIs
- API Authentication and Security
- Backend Integration Concepts
- Creating a Simple User Interface
- Managing Environment Variables
- Testing Generative AI Applications
- Deployment Fundamentals
- Monitoring AI Applications
- Generative AI Deployment Project
Responsible Generative AI and Live Projects
Evaluate bias, privacy, reliability, security and intellectual-property concerns while documenting human-review needs.
- Introduction to Responsible Generative AI
- AI Bias and Fairness
- Privacy and Data Protection
- Hallucinations and Reliability
- Copyright and Intellectual Property Concepts
- AI Safety and Security Basics
- Evaluating AI Outputs
- Building a Responsible AI Workflow
- End-to-End Generative AI Application
- Final Generative AI Capstone Project
Project practice ideas
Use these examples to discuss suitable practice work with your trainer.
Information chatbot
Create a small assistant that answers a defined set of questions and test its limitations.
Document assistant prototype
Connect a document workflow to AI and evaluate answers against the source material.
AI content workflow
Design a drafting, summarization or editing workflow with clear audience, format and evaluation criteria.
Generative media project
Plan image or video prompts, review output consistency and document responsible-use considerations.
AI agent project
Connect a limited set of tools or APIs to a defined workflow and test expected inputs, errors and human-review points.
Generative AI deployment project
Connect a model API to a simple application, manage configuration and document deployment and monitoring requirements.
Final Generative AI capstone
Build an end-to-end application combining prompts, retrieval or tools, then evaluate accuracy, usefulness and limitations.
Roles to explore
Build a portfolio and practise explaining your work. Role requirements depend on the employer, your skills and experience.
- AI application trainee
- Automation developer trainee
- AI-assisted content specialist
Frequently asked questions
Is coding required to start Generative AI?
You can begin with AI tools and prompt engineering without coding. Python and API skills become important when you want to build custom assistants, integrations or automation.
How is Generative AI different from Data Science and Machine Learning?
Generative AI focuses on language models, prompting, APIs and applications that generate or transform content. Data Science and Machine Learning focus more on data analysis, predictive models, clustering and model evaluation.
Are retrieval and document assistants covered?
Yes. The source curriculum includes embeddings, vector databases, document processing, Retrieval Augmented Generation and document-based chatbots.
Does the course include agents and tool calling?
Yes. Agent concepts cover tools and function calling, API integration, memory and multi-step workflows.
Are image and video generation topics included?
Yes. The curriculum introduces text-to-image and text-to-video models, prompt design, editing workflows and responsible generated-media use.
How are application reliability and privacy handled?
The course includes output evaluation, hallucinations, privacy, security and responsible workflows. Projects should document limitations and identify situations requiring human review.
Who is this course for?
Beginners can start with AI tools and prompting. Learners who want to build applications should add Python and API foundations.
How can I confirm fees, duration and batch timings?
Contact Groot Academy for the current syllabus, fees, duration and available learning modes before enrolling. These details depend on the selected course and batch.
Ready to discuss Generative AI & AI Tools?
Ask the course team about syllabus coverage, current batch timings, fees, duration and learning modes before you decide.