Generative AI & Agentic AI
The Generative AI & Agentic AI Training program is designed to provide learners with practical knowledge of modern Artificial Intelligence technologies, including Generative AI, Large Language Models (LLMs), Prompt Engineering, Retrieval-Augmented Generation (RAG), AI Agents, LangChain, vector databases, and AI application development.
The course focuses on building real-world AI solutions that can understand information, generate content, retrieve knowledge, use external tools, perform tasks, and make intelligent decisions.
Learners will gain hands-on experience in developing AI-powered applications, chatbots, intelligent assistants, RAG systems, and autonomous AI agents using industry-relevant technologies.
Who Can Join This Course?
This course is suitable for:
- Students & Fresh Graduates
- Software Developers
- Python Developers
- Data Analysts
- Data Engineers
- Machine Learning Aspirants
- AI Aspirants
- Cloud Professionals
- IT Professionals
- Business Analysts
- Automation Professionals
- Working Professionals
Professionals looking to transition into AI careers
This course provides comprehensive training in the rapidly evolving field of Generative AI and Agentic AI.
Learners will begin with the fundamentals of Artificial Intelligence and Machine Learning before progressing into Generative AI, Large Language Models, Transformer architecture, Prompt Engineering, embeddings, vector databases, RAG pipelines, and AI agents.
The course also introduces popular frameworks and technologies such as Python, OpenAI APIs, Hugging Face, LangChain, LangGraph, vector databases, and cloud-based AI services.
Through practical exercises and real-world projects, learners will understand how to build AI applications capable of generating content, answering questions from private data, using tools and APIs, maintaining context, and executing multi-step tasks.
Why Choose Our Generative AI & Agentic AI Training?
- Industry-oriented curriculum
- Hands-on practical training
- Python for AI application development
- Generative AI & LLM concepts
- Prompt Engineering
- AI API integration
- Embeddings & Vector Databases
- RAG application development
- LangChain & LangGraph
- AI Agents & Tool Calling
- Multi-Agent Systems
- AI Chatbot Development
- Real-world AI projects
- End-to-end capstone project
- AI application deployment concepts
- Career-focused learning approach
- Generative AI Fundamentals
Understand the concepts, architecture, applications, and business use cases of Generative AI. - Large Language Models (LLMs)
Learn how modern language models work and how they can be integrated into AI applications. - Prompt Engineering
Design effective prompts to improve the accuracy, consistency, and quality of AI-generated responses. - Python for AI Development
Develop practical Python skills required to build and integrate AI applications. - AI APIs & Model Integration
Learn how to integrate modern AI models into applications using APIs and SDKs. - Embeddings & Vector Databases
Understand semantic search, embeddings, similarity search, and vector-based information retrieval. - Retrieval-Augmented Generation (RAG)
Build AI applications that retrieve information from external knowledge sources before generating responses. - AI Agents
Develop intelligent agents capable of reasoning, using tools, accessing information, and completing multi-step tasks. - LangChain & LangGraph
Build structured LLM applications, workflows, and agentic systems using modern AI frameworks. - Real-World AI Projects
Apply Generative AI and Agentic AI concepts through practical projects and business use cases.
By the end of this course, learners will be able to:
- Understand Artificial Intelligence and Generative AI fundamentals.
- Understand Large Language Models and their applications.
- Work with modern AI models and APIs.
- Write effective prompts using Prompt Engineering techniques.
- Develop AI applications using Python.
- Understand tokens, embeddings, context windows, and model parameters.
- Build semantic search applications using embeddings.
- Work with vector databases.
- Build Retrieval-Augmented Generation (RAG) applications.
- Develop AI-powered chatbots and intelligent assistants.
- Integrate external tools and APIs with AI applications.
- Build AI agents capable of performing multi-step tasks.
- Develop agent workflows using LangChain and LangGraph.
- Implement memory and context management in AI applications.
- Evaluate and improve AI application performance.
- Build and deploy real-world Generative AI and Agentic AI projects.
Learners are recommended to have:
- Basic computer knowledge.
- Basic programming knowledge.
- Basic understanding of Python is recommended.
- Basic understanding of databases is helpful.
- Basic knowledge of APIs and web technologies is beneficial.
- No prior Generative AI experience is required.
Job Roles / Industry Positions
- Generative AI Engineer
- AI Engineer
- AI Developer
- Generative AI Developer
- Agentic AI Engineer
- AI Agent Developer
- LLM Engineer
- LLM Application Developer
- Prompt Engineer
- RAG Developer
- AI Application Developer
- Machine Learning Engineer
- Conversational AI Developer
- AI Automation Engineer
- AI Solutions Engineer
- AI Software Engineer
- NLP Engineer
- AI Consultant
- AI Research Engineer
- AI Product Engineer
- Introduction to Artificial Intelligence
- What is Artificial Intelligence?
- AI Evolution
- AI Applications
- Machine Learning Overview
- Deep Learning Overview
- Natural Language Processing
- Computer Vision Overview
- AI vs Machine Learning vs Deep Learning
- Introduction to Generative AI
- Introduction to Agentic AI
- Real-World AI Applications
- Python for AI
- Python Fundamentals
- Variables & Data Types
- Operators
- Conditional Statements
- Loops
- Functions
- Lists, Tuples & Dictionaries
- Object-Oriented Programming Basics
- Exception Handling
- File Handling
- Working with JSON
- Python Libraries
- API Integration
- Python for AI Application Development
- Generative AI Fundamentals
- What is Generative AI?
- Generative AI Architecture
- Generative AI Use Cases
- Text Generation
- Image Generation
- Code Generation
- Audio & Video Generation
- Generative AI Applications
- AI Models & Model Providers
- Open-Source vs Closed-Source Models
- Challenges and Limitations of Generative AI
- Large Language Models (LLMs)
- Introduction to LLMs
- How LLMs Work
- Transformer Architecture
- Tokens & Tokenization
- Context Windows
- Parameters
- Attention Mechanism
- Training & Fine-Tuning Concepts
- Inference
- Temperature & Model Parameters
- LLM Capabilities & Limitations
- LLM Use Cases
- Prompt Engineering
- Introduction to Prompt Engineering
- Prompt Structure
- Zero-Shot Prompting
- Few-Shot Prompting
- Role-Based Prompting
- Chain-of-Thought Concepts
- Instruction Prompting
- Contextual Prompting
- Structured Output
- Prompt Templates
- Prompt Optimization
- Prompt Evaluation
- Best Practices for Effective Prompts
- Working with AI APIs
- Introduction to AI APIs
- API Authentication
- API Requests & Responses
- Working with AI SDKs
- Text Generation APIs
- Chat Completion
- Structured Responses
- Function Calling
- Streaming Responses
- Error Handling
- Building AI Applications Using APIs
- Embeddings & Semantic Search
- Introduction to Embeddings
- Text Embeddings
- Semantic Similarity
- Vector Representations
- Similarity Search
- Cosine Similarity
- Embedding Models
- Document Embeddings
- Query Embeddings
- Semantic Search Applications
- Vector Databases
- Introduction to Vector Databases
- Why Vector Databases?
- Vector Storage
- Similarity Search
- Indexing
- Metadata Filtering
- Vector Database Architecture
- Working with Popular Vector Databases
- Storing Embeddings
- Retrieving Relevant Information
- Retrieval-Augmented Generation (RAG)
- Introduction to RAG
- Why RAG?
- RAG Architecture
- Document Loading
- Text Splitting
- Chunking Strategies
- Embedding Generation
- Vector Storage
- Retrieval
- Context Injection
- Response Generation
- RAG Pipeline Development
- RAG Evaluation
- Advanced RAG Concepts
- LangChain
- Introduction to LangChain
- LangChain Architecture
- Models
- Prompts
- Chains
- Retrievers
- Document Loaders
- Text Splitters
- Embeddings
- Vector Stores
- Memory
- Tools
- Agents
- Building LLM Applications with LangChain
- AI Chatbot Development
- Introduction to AI Chatbots
- Conversational AI
- Chatbot Architecture
- Prompt Design
- Context Management
- Conversation Memory
- Document-Based Chatbots
- RAG-Based Chatbots
- API Integration
- Building Intelligent Chatbots
- Chatbot Deployment Concepts
- Introduction to Agentic AI
- What is Agentic AI?
- Generative AI vs Agentic AI
- AI Agents
- Agent Architecture
- Agent Components
- Reasoning & Planning
- Tool Usage
- Decision Making
- Memory
- Context Management
- Autonomous Task Execution
- Agentic AI Use Cases
- AI Agents & Tool Calling
- Introduction to AI Agents
- Agent Workflows
- Tool Calling
- Function Calling
- API Integration
- Web Search Tools
- Database Tools
- File Processing Tools
- External Service Integration
- Tool Selection
- Multi-Step Task Execution
- Error Handling in Agents
- LangGraph
- Introduction to LangGraph
- Graph-Based AI Workflows
- Nodes & Edges
- State Management
- Agent Workflows
- Conditional Workflows
- Human-in-the-Loop
- Multi-Step Agents
- Multi-Agent Workflows
- Agent Memory
- Building Agentic Applications
- Multi-Agent Systems
- Introduction to Multi-Agent AI
- Single Agent vs Multi-Agent Systems
- Agent Roles
- Agent Communication
- Task Delegation
- Collaborative Agents
- Supervisor Agents
- Specialized Agents
- Multi-Agent Workflow Design
- Real-World Multi-Agent Applications
- AI Memory & Context
- Introduction to AI Memory
- Short-Term Memory
- Long-Term Memory
- Conversation History
- Context Management
- Memory Storage
- Retrieval-Based Memory
- Personalization Concepts
- Managing Large Contexts
- Context Optimization
- AI Application Development
- AI Application Architecture
- Frontend & Backend Integration
- AI API Integration
- Database Integration
- RAG Integration
- Agent Integration
- Authentication
- Logging
- Error Handling
- Application Testing
- AI Application Optimization
- AI Security & Responsible AI
- AI Security Fundamentals
- Prompt Injection
- Data Privacy
- Sensitive Data Protection
- AI Hallucinations
- Model Limitations
- Output Validation
- Responsible AI
- Ethical AI Development
Secure AI Application Design
- AI Evaluation & Optimization
- AI Application Evaluation
- Response Quality
- Accuracy & Relevance
- Hallucination Detection
- Prompt Evaluation
- RAG Evaluation
- Agent Evaluation
- Latency Optimization
- Cost Optimization
- Model Selection
- Performance Monitoring
- Deployment & Cloud AI
- AI Application Deployment
- REST API Integration
- Docker Basics for AI Applications
- Cloud Deployment Concepts
- Environment Configuration
- Secrets Management
- Application Monitoring
- AI Application Scaling
- Production Deployment Best Practices
- Real-World Projects
Learners will work on practical Generative AI and Agentic AI projects such as:
AI Document Assistant
- Upload Documents
- Document Processing
- Embedding Generation
- Vector Database
- RAG Pipeline
- Question Answering
- AI-Powered Document Search
Intelligent Customer Support Chatbot
- Customer Query Processing
- Conversational AI
- Knowledge Base Integration
- RAG Implementation
- Context Management
- Automated Response Generation
AI Resume & Career Assistant
- Resume Processing
- Resume Analysis
- Job Description Analysis
- Skill Matching
- AI Recommendations
- Career Suggestions
AI Research Assistant
- Information Retrieval
- Document Analysis
- Summarization
- Question Answering
- Source-Based Responses
- AI Research Workflow
AI Data Analyst Agent
- Data Upload
- Data Understanding
- Natural Language Queries
- Data Analysis
- Automated Insights
- Chart Generation
- AI-Powered Reporting
Agentic AI Automation System
- Task Understanding
- Planning
- Tool Selection
- API Integration
- Multi-Step Execution
- Agent Memory
- Automated Task Completion
- Capstone Project
Learners will complete an end-to-end Generative AI & Agentic AI Capstone Project covering:
- Business Problem Identification
- AI Solution Design
- Data Collection
- Data Preparation
- Prompt Engineering
- LLM Integration
- Embedding Generation
- Vector Database Integration
- RAG Pipeline Development
- AI Agent Development
- Tool & API Integration
- Memory & Context Management
- AI Application Development
- Testing & Evaluation
- Security & Optimization
- Deployment
- Final Project Presentation
After completing the course, learners can explore roles such as:
- Generative AI Developer
- AI Engineer
- Generative AI Engineer
- AI Application Developer
- LLM Engineer
- AI Agent Developer
- Machine Learning Engineer
- Prompt Engineer
- RAG Developer
- AI Automation Engineer
- Conversational AI Developer
- AI Solutions Engineer
- Python AI Developer
- Junior AI Engineer
