Generative AI

Join Advance Generative AI to master the art of building intelligent, creative, and scalable AI systems! Learn how to harness large language models (LLMs), fine-tune transformers, and deploy AI solutions on the cloud, sharpen your skills in prompt engineering, multimodal AI, and intelligent automation, and build real-world applications - all while exploring how Gen AI is revolutionizing software, design, and the future of human-computer interaction.

Saksham Arora

Saksham Arora

Advance

Generative AI & LLMs
Our Course Benefits
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Generative AI Mastery

Multimodal AI

Applied AI Projects

AI-Driven Automation

Prompt Engineering & LLMs

Cloud & AI Deployment

AI Ethics & Safety

Mentorship & Capstone Projects

Career Sectors & Job Roles
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AI Product Development

Enterprise AI & SaaS

AI Infrastructure & Platforms

AI Ethics & Governance

Machine Learning & Research

Product Companies & Startups

Data & Knowledge Systems

Freelancing & Remote Work

What to expect from this course ?
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Advance Generative AI is your gateway to becoming an AI-powered innovator capable of designing, building, and deploying next-generation intelligent applications. This comprehensive program takes you from the foundations of large language models (LLMs) and prompt engineering to mastering fine-tuning, multimodal AI, and scalable AI deployment on the cloud.

You'll start by learning the core principles of LLMs and prompt design, then dive into model fine-tuning, embeddings, and Retrieval-Augmented Generation (RAG) for building production-ready AI systems. From there, you'll explore multimodal AI (text, vision, speech), integrate vector databases for knowledge retrieval, and deploy applications on cloud platforms like AWS, Azure, and GCP. Along the way, you'll also gain expertise in AI ethics, safety, and responsible AI practices while exploring how agentic AI and automation are reshaping modern industries.

Through hands-on labs, real-world projects, and expert mentorship, you'll gain the ability to create, scale, and integrate AI applications with confidence.

By the end of the course, you'll have the technical depth, applied portfolio, and interview-ready confidence to excel as a Generative AI Engineer, LLM Specialist, AI Application Developer, or Applied Researcher across tech companies, SaaS platforms, startups, and enterprise AI teams.

With a structured curriculum, industry-grade projects, and career-focused guidance, Advance Generative AI is designed to make you a future-ready AI professional, equipped to thrive in the era where Gen AI is transforming software engineering, business workflows, and human-computer interaction.

The Curriculum
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  • What are Large Language Models (LLMs)? Architecture, Training & Deployment.
  • Generative vs Discriminative Models. What are Transformers & their role in LLMs? How do they work?
  • Introduction to Prompt Engineering - principles, techniques (Zero-shot, few-shot, Chain-of-Thought, etc).
  • Crafting effective prompts for different use cases (content generation, summarization, Q&A, etc).
  • Exploring various LLMs like GPT, Llama, Falcon, Claude.
  • Practical labs on prompt engineering & prompt design.

  • Multimodal Models - an introduction
  • Vision Transformers & diffusion models (Stable Diffusion, DALL-E)
  • Image & Video generation from text prompts.
  • Voice cloning, Speech-to-Text, and Text-to-Speech (TTS).
  • Building multimodal applications with frameworks like Gradio.
  • Practical labs on building multimodal applications.

  • Building production-ready applications.
  • Fine-tuning vs Prompt Engineering.
  • Data collection, cleaning, and preparation for fine-tuning.
  • Fine-tuning a pre-trained model on a custom dataset.
  • Deployment strategies for fine-tuned models.
  • Practical lab on fine-tuning a model for a specific task.

  • What is Retrieval-Augmented Generation (RAG)?
  • Vector Databases & Embeddings.
  • Building a RAG system for knowledge retrieval.
  • Handling complex queries and improving retrieval accuracy.
  • Case studies of RAG in real-world applications.
  • Practical labs on building a RAG system.

  • Cloud deployments on AWS, Azure, GCP.
  • LLM APIs (OpenAI, Hugging Face, etc).
  • LangChain, LlamaIndex, and other LLM orchestration frameworks.
  • Building agents with LLMs & function calling.
  • Integrating LLMs into existing applications.
  • Practical labs on deployment of LLM applications.

  • Ethical considerations in AI development.
  • Bias in AI models and how to mitigate it.
  • Responsible AI practices.
  • AI Safety & Governance frameworks.
  • Case studies of AI failures and how to prevent them.
  • Practical labs on AI safety & governance.

  • API Usage: OpenAI, Anthropic, Hugging Face APIs
  • Deployment: Quantization, GPUs, inference servers (vLLM, TensorRT, Ollama)
  • Application Architecture: Microservices, caching, monitoring, A/B testing

  • RAG Architecture: Retrieval + Generation pipelines, chunking, embeddings
  • Vector Databases: Pinecone, Weaviate, Chroma, FAISS
  • Advanced Techniques: Hybrid search, multi-hop reasoning, context compression

  • Agents: ReAct, multi-agent systems, memory & state management
  • Tool Use: Function calling, APIs, code execution, safety mechanisms
  • Advanced Agents: Web browsing, multimodal agents, human-in-the-loop

  • Ethics: Bias, fairness, transparency, explainability
  • Safety & Security: Jailbreaking, prompt injection, red-teaming, content moderation
  • Production: Versioning, monitoring, compliance, cost optimization

  • Evolution from AI Assistants → AI Agents
  • Agentic AI Fundamentals
  • ReAct Framework
  • Reflection Agents
  • Planning Agents
  • Autonomous Agents
  • CrewAI
  • AutoGen
  • LangGraph
  • Semantic Kernel
  • Memory Management
  • Long-term Memory
  • Episodic Memory
  • Multi-Agent Communication
  • Multi-Agent Collaboration
  • Hierarchical Agents
  • Swarm Intelligence
  • Human-in-the-loop
  • Enterprise AI Agents
  • AI Workforce Design
  • Projects: AI Software Company, AI Recruitment Team, AI Research Assistant, AI Customer Support Team, AI Sales Agent

  • What is MCP?
  • MCP Architecture
  • MCP Clients
  • MCP Servers
  • MCP Tools
  • MCP Resources
  • MCP Prompts
  • Building Custom MCP Servers
  • Integrating AI with: Gmail, Slack, Jira, GitHub, Notion, Google Drive, Databases, CRMs
  • Remote MCP
  • Security
  • Authentication
  • Enterprise MCP
  • Project: Enterprise AI Assistant with MCP

  • vLLM
  • TensorRT-LLM
  • Ollama
  • LM Studio
  • SGLang
  • Triton Inference Server
  • Model Serving
  • Quantization
  • GGUF
  • GPU Optimization
  • CUDA Basics
  • Distributed Inference
  • High Throughput Serving
  • Autoscaling
  • Cost Optimization
  • Projects: Deploy production-grade LLM server

  • LLMOps
  • LangSmith
  • Phoenix
  • Weights & Biases
  • MLflow
  • Prompt Versioning
  • Model Versioning
  • Dataset Versioning
  • AI Monitoring
  • Hallucination Detection
  • Latency Monitoring
  • Cost Tracking
  • Evaluation Pipelines
  • DeepEval
  • RAGAS
  • Benchmarking
  • A/B Testing
  • Projects: Production Monitoring Dashboard

  • Graph RAG
  • Agentic RAG
  • Hybrid Search
  • Context Compression
  • Multi-hop Retrieval
  • Query Transformation
  • Parent Document Retrieval
  • Knowledge Graphs
  • Adaptive RAG
  • Self-RAG
  • Corrective RAG (CRAG)
  • RAPTOR
  • Long Context Retrieval
  • Projects: Enterprise Knowledge Assistant

  • LoRA
  • QLoRA
  • PEFT
  • RLHF
  • DPO
  • SFT
  • Dataset Engineering
  • Synthetic Data
  • Distillation
  • Model Compression
  • Projects: Fine-tune Llama 3

  • Prompt Injection
  • Jailbreak Prevention
  • AI Red Teaming
  • Guardrails
  • PII Detection
  • Data Leakage
  • Secure RAG
  • Secure Agents
  • AI Governance
  • Compliance
  • GDPR
  • SOC2
  • HIPAA
  • Projects: Secure Enterprise AI Chatbot

  • AI System Design
  • AI Architecture
  • AI SaaS
  • AI Workflow Automation
  • AI APIs
  • AI Product Lifecycle
  • AI UX
  • AI Cost Optimization
  • AI Monetization
  • Projects: Build an AI SaaS
AI Tools & Frameworks You'll Master
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LLMs

GPT-4o / GPT-5 APIs

Claude

Gemini

Llama

Mistral

Qwen

Agent Frameworks

LangChain

LangGraph

CrewAI

AutoGen

Semantic Kernel

OpenAI Agents SDK

RAG & Data

LlamaIndex

FAISS

ChromaDB

Pinecone

Weaviate

Milvus

MCP & Integrations

Model Context Protocol (MCP)

GitHub

Gmail

Slack

Jira

Notion

Google Drive

PostgreSQL

MongoDB

Deployment & Inference

Ollama

vLLM

TensorRT-LLM

Triton Inference Server

Docker

Kubernetes

AWS

Azure

GCP

Evaluation & LLMOps

LangSmith

DeepEval

RAGAS

MLflow

Phoenix

Weights & Biases

Development Tools

Python

FastAPI

Streamlit

Gradio

Git

GitHub Actions

AI Productivity

Cursor

GitHub Copilot

Claude Code

OpenAI Codex

Windsurf

Certificate of Completion
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Certficiate of Completion
Tushar Yadav

Tushar Yadav

Software Engineer @ Paytm

1.5+ Yeras Experience

+352%

Salary Hike

software trainee Software engineer

"The Generative AI program at CodeKerdos exceeded my expectations. The mentors were incredibly helpful, the content was practical and industry-focused, and the support from the entire team was outstanding. The hands-on learning approach helped me gain confidence and accelerate my career growth. Highly recommended!"

Ashutosh Pandey

Ashutosh Pandey

Senior Engineer @ IBM

1.5+ Years Experience

+187%

Salary Hike

Software trainee Software engineer

"What stood out most was the quality of the live classes and mentors. The sessions were engaging, industry-focused, and filled with practical insights. The mentors were always supportive, making the learning journey smooth and helping me gain the confidence needed for career advancement."

Generative AI Case Study Projects

Explore innovative projects that showcase the power of generative AI. These case studies highlight real-world applications, demonstrating how generative AI can transform industries, enhance creativity, and solve complex problems. Dive into the details of each project to understand the methodologies, technologies used, and the impact achieved.

Enterprise RAG System

Build a production-ready Retrieval-Augmented Generation (RAG) pipeline with advanced features such as hybrid search (vector + keyword), multi-turn conversation memory, and enterprise-level authentication for accurate knowledge retrieval.

AI Agent Platform

Develop a multi-agent collaboration platform where specialized agents (planner, researcher, executor, validator) work together to solve complex problems like code generation, code debugging, or business workflows.

Fine-tuned Domain Model

Train a specific domain (finance, healthcare, legal, or education) and fine-tune a foundation LLM for domain-specific tasks like text classification or compliance checks, then deploy it with APIs.

AI-Powered Development Assistant

Create a coding productivity tool with features like chat, complex code debugging suggestions, text generation, and inline documentation powered by LLMs and real-time developer context.

Multimodal AI Application

Design an app that combines text, images, and audio - e.g., generate presentations from text prompts, create an audio (podcast) with voice clones, or build an “AI lecture companion” with slides + narration.

Knowledge Graph + LLM Hybrid

Integrate graph databases with LLMs to build an intelligent assistant capable of reasoning over structured + unstructured data. Use cases: enterprise knowledge search, fraud detection, or academic research aid.

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Get the complete course details in our brochure.

Discover all the essential information about our courses in our detailed brochure. Get insights on curriculum, schedules, and enrollment options to help you make the best choice for your education.

Ready to Start Your Generative AI Journey?

Learn Generative AI from the ground up, covering prompt engineering, large language models, AI tools, and real-world use cases. Build hands-on projects using modern AI frameworks, integrate AI into web applications, and gain practical skills needed to stay ahead in the rapidly evolving AI-driven tech industry.

Monthly EMI options upto (24) Months
Monthly EMI options upto (24) Months

Flexible monthly EMI plans available for up to 24 months.

Modes of Payment ( UPI, Cards, Wallet, Net Banking)
Modes of Payment ( UPI, Cards, Wallet, Net Banking)

Explore the various modes of payment available today: UPI for instant transfers, cards for secure transactions, wallets for convenience, and net banking for easy online management. Each option offers unique benefits to suit your needs.

Course Fees

89,999

Final pricing refers to the last and definitive cost of a product or service, including all applicable fees and discounts.

Includes:

  • Live sessions
  • Recorded videos
  • Study material / PDFs
  • Assignments & projects
  • 1:1 mentorship / doubt sessions
  • Certification upon completion