Duration: 60 Hours
Mode of Training: Online
Level: Advanced

Overview

Cygnisoft offers Generative AI (GenAI) Training for learners who want clear, practical, and job-relevant AI skills. This training focuses on real-world application rather than hype or heavy theory.

Many people hear about AI but feel overwhelmed by the number of tools and trends. It’s often unclear what truly matters and how AI can be used effectively in real work environments.

This course removes that confusion step by step. In 2026 and beyond, organizations are looking for professionals who can apply AI meaningfully. This training helps you build those skills with clarity and confidence.

What You Will Learn

You will learn how Generative AI works and how to use it safely, responsibly, and effectively.

  • How AI generates text, images, and code
  • How prompts guide and improve AI responses
  • How AI supports automation and reporting
  • How AI integrates with real business workflows
  • How to test, evaluate, and improve AI outputs

The course builds strong conceptual understanding first, then moves into hands-on practical use.

Project-Based Learning

This training is designed around hands-on, project-based learning.

You will work on:

  • AI chatbots and assistant systems
  • Automation for daily business tasks
  • Simple AI-driven workflows
  • Real-world use cases

You won’t just watch demos. You will build, test, and refine solutions yourself.

Learning Format

This Generative AI training is delivered fully online with live instructor guidance and continuous support. Learners can join from anywhere and benefit from an interactive learning experience without the need for travel.

The flexible format is ideal for professionals, students, and beginners who want hands-on AI training that fits into their schedules while maintaining a strong project focus.

Why Choose Cygnisoft?

Cygnisoft focuses on building long-term, adaptable AI skills rather than short-term trends. The goal is to help learners understand Generative AI in a way that remains useful even as tools and technologies evolve.

What makes this training trustworthy:

  • Experienced trainers with deep expertise in technology, data, and AI systems
  • Regularly updated curriculum aligned with current industry practices
  • Course content refreshed to reflect major technical advancements
  • A learning path that builds understanding first, then practical capability
  • Live guidance and doubt-clearing support throughout the course

This approach ensures you stay relevant even as AI tools and platforms change.

Thousands of learners, including freshers and working professionals, have completed this GenAI training and successfully applied it in real-world scenarios.

Career Value

Generative AI skills are increasingly valuable across product, marketing, operations, analytics, and data-driven teams. Professionals who can apply AI effectively are becoming essential to modern organizations.

Who Is This For?

This training is suitable for:

  • Students and beginners exploring AI
  • Working professionals looking to upskill
  • Developers and technical professionals
  • Anyone seeking practical, structured Generative AI training

Start Your Learning Journey

You learn, you practice, and you grow. With Cygnisoft, you gain the clarity and confidence needed to use Generative AI meaningfully in real-world work environments.

Generative AI (GenAI) Course Overview

Module 1: Introduction to Generative AI +

  • Overview of Generative AI
  • Generative AI vs. Traditional AI
  • Use Cases
  • Understanding AI: Basics and Use Cases
  • Differentiating ML, DL and AI
  • Basics of NLP

Module 2: Large Language Models +

  • Introduction to OpenAI and LLMs
  • What are LLMs?
  • How do LLMs work?
  • Types of LLMs
  • Practical uses of LLM
  • Text Generation
  • Chatbot Creation
  • Foundations of Generative Models & LLM
  • Generative Adversarial Networks (GANs)
  • Autoencoders in Generative AI
  • Significance of Transformers in AI
  • Attention is All You Need – Transformer Architecture
  • Reinforcement Learning RLHF

Module 3: LLMs Foundation Models +

  • Encoder Models i.e. BERT
  • Decoder Models GPT
  • Encoder Decoder Model i.e. T5
  • Open-Source Models vs Commercial Models
  • Quantization Models (GGML Vs GGUF)

Module 4: LangChain & Lama Index Framework +

  • Introduction to LangChain & Lamaindex
  • Hugging Face API + Langchain
  • Hugging Face API + Lamaindex
  • Memory in Langchain
  • LLM Chain

Module 5: Retrieval Augmented Generation (RAG) And Evaluating RAG +

  • Overview of RAG
  • Intro to Semantic Search, Vectors and Vector Databases
  • How to build a Gen AI app with RAG
  • Evaluating RAG
  • End to End Medical Chatbot Project

Module 6: Advance RAG +

  • LOTR
  • COT

Module 7: Fine Tuning and Evaluating LLMs +

  • Instruction fine-tuning
  • Fine-tuning on a single task
  • Multi-task instruction fine-tuning
  • Model evaluation
  • Benchmarks
  • Parameter efficient fine-tuning (PEFT)
  • PEFT techniques 1: LoRA
  • PEFT techniques 2: Soft prompts
  • Lab 2 walkthrough

Module 8: Evaluation Matrix +

  • Rouge1
  • BLEU
  • Meteor
  • CIDEr

Module 9: Deployment And Hardware Considerations +

  • Deployment Strategies
  • Hardware Requirements

Module 10: Introduction To Generative AI On Cloud: Hugging Face / Hugging Face Hub +

  • Hugging Face
  • Hugging Face Overview

Module 11: Generative AI On Cloud - GCP +

  • Model Evaluation
  • Prompt Design

Module 12: Generative AI On Cloud - Azure +

  • Azure ML
  • Azure Cognitive Services
  • Azure Databricks

Module 13: Generative AI On Cloud - AWS +

  • AWS Bedrock

Module 14: Introduction To ChatGPT & Architectures +

  • Introduction to GPT and ChatGPT
    • Overview of GPT
    • ChatGPT Capabilities
    • GPT Architecture
  • Understanding GPT-3, GPT 3.5, and GPT-4
    • GPT-3 vs GPT-4
    • Advancements in GPT-4
    • Ethical Considerations

Module 15: Introduction To Prompt Engineering +

  • The Fundamentals of Prompt Engineering
    • What is prompt engineering
    • Its importance
    • Types of prompts
  • Content Generation with Prompts
    • Strategies for generating text
    • Video scripts
    • Music using prompts
  • Tokens and Parameters in AI
    • The role and understanding of tokens
    • Introduction to prompt parameters

Module 16: Advanced Prompt Techniques +

  • Zero-Shot to Few-Shot Learning
    • Deep dive into zero-shot
    • One-shot
    • Few-shot learning
  • Fine-Tuning AI Model Parameters
    • Introduction to model parameter adjustments
  • Hallucinations and Bias in AI
    • Strategies for managing AI hallucinations and biases
  • Advanced Prompt Engineering Techniques
    • Methods for crafting complex prompts
    • Incorporating creativity and context
  • Refining and Optimizing Prompts
    • Techniques for prompt refinement and iterative improvement

Module 17: Agents +

  • Overview of Agents
  • LangChain Agents
  • AWS Bedrock Agents
  • Crew AI

Module 18: Guardrails +

  • Overview of Guardrails
  • Implement Guardrails
  • AWS, Azure Guardrails

Module 19: Github Co-Pilot +

  • Overview of GitHub Co-Pilot
  • Practice Copilot with complex coding projects
  • Generate, document, explain and test code in a few seconds with effective prompting
  • Leverage GitHub Copilot’s capabilities to write better code, faster
  • Commands
  • Agents

Module 20: Generative AI for Software Testing +

  • Generate Documentation (Test Cases, Artifacts) using Gen AI
  • Leveraging AI for understanding Test Scripts
  • Generate Test Cases & Test Scripts from an Image
  • Developing a QA Strategy with Generative AI

Module 21: Real World Applications and Case Studies +

  • End to End Gen AI Project using Google Gemini Pro
  • Medical Chatbot
  • Talk to your codebase
  • Code Translation and Conversion
  • Chatbot to talk to complex pdf
  • Self-healing code
  • NLP to SQL