Artificial Intelligence

Certified Generative AI Professional

4.9

Foundation Course

3 Months

Hybrid (Physical & Online)

Home Courses Certified Generative AI Professional

About This Course

Welcome to the Certified Generative AI Professional program! This cutting-edge 3-month course is designed to transform you into a skilled, job-ready Generative AI practitioner — capable of building, fine-tuning, deploying, and operationalising large language models, image generation systems, and AI-powered applications across real-world business and technical contexts.

Generative AI is the most disruptive technology of the decade. From GPT-4 and Claude to Stable Diffusion and Midjourney, businesses across every sector are racing to integrate generative AI into their products, workflows, and services. The professionals who understand how to work with these systems at a technical level — prompt engineering, RAG pipelines, fine-tuning, LLM API integration, and AI agent development — are among the most sought-after in the global technology market. This program gives you that expertise, grounded in hands-on project work with real AI systems and production deployment scenarios.

Why Choose This Course?

Hands-On with Real AI Systems: Work directly with OpenAI GPT-4o, Anthropic Claude, Google Gemini, Llama 3, Mistral, and Stable Diffusion — not just theory, but live API integration, prompt engineering, and model interaction throughout the program.
RAG and LLM Application Development: Master Retrieval-Augmented Generation (RAG) pipelines and LangChain to build intelligent, context-aware AI applications that go far beyond basic chatbots.
Fine-Tuning and Model Customisation: Learn how to fine-tune open-source LLMs on custom datasets using LoRA and QLoRA techniques — a skill that very few developers possess and that commands premium salaries.
AI Agents and Automation: Build autonomous AI agents with tool use, memory, and multi-step reasoning using LangChain Agents and AutoGen — the next frontier in enterprise AI automation.
Image and Multimodal AI: Explore image generation with Stable Diffusion and DALL-E 3, multimodal model capabilities, and how to build AI systems that work across text, image, and audio modalities.
Career Support: AI portfolio guidance, CV review, LinkedIn profile optimisation, and access to our partner network of AI-first startups and technology companies actively hiring generative AI professionals.

Tools & Technologies Covered

OpenAI API (GPT-4o, GPT-4 Vision, DALL-E 3, Whisper, Embeddings) for LLM application development
Anthropic Claude and Google Gemini APIs for multi-provider LLM integration strategies
LangChain and LlamaIndex for building RAG pipelines, document QA systems, and LLM orchestration
Hugging Face Transformers and Datasets library for accessing and fine-tuning open-source models
LoRA and QLoRA (PEFT library) for parameter-efficient fine-tuning of large language models
Stable Diffusion (via Automatic1111 and ComfyUI) for image generation, ControlNet, and inpainting
LangChain Agents and AutoGen for building autonomous multi-step AI agent systems
ChromaDB, Pinecone, and FAISS for vector database storage and similarity search in RAG systems
FastAPI and Streamlit for building and deploying AI-powered web applications and APIs
Python, Jupyter Notebooks, and Google Colab as the primary development and experimentation environment
Docker and Hugging Face Spaces for containerising and deploying generative AI models and applications

Hands-On Projects

Throughout the 3 months you will build a professional generative AI portfolio including:

Custom AI Chatbot with RAG: Build a domain-specific chatbot that ingests your own documents, stores embeddings in a vector database, and answers questions with accurate, cited, context-aware responses using LangChain and ChromaDB.
LLM Fine-Tuning Pipeline: Fine-tune an open-source LLM (Llama 3 or Mistral) on a custom dataset using LoRA/QLoRA, evaluate the model against the base model, and document the training pipeline end-to-end.
AI Image Generation System: Build a Stable Diffusion image generation application with custom prompt engineering, ControlNet conditioning, style consistency, and a Streamlit frontend for user interaction.
Autonomous AI Agent: Design and build a multi-tool AI agent using LangChain Agents that can search the web, read documents, execute code, and complete multi-step tasks autonomously.
Capstone: Production AI Application: Design, build, and deploy a complete generative AI-powered application — an AI writing assistant, AI customer support system, or AI content generation pipeline — with a FastAPI backend, Streamlit frontend, and Docker deployment.

Who This Course Is For

Software developers and Python programmers who want to specialise in generative AI application development
Data scientists and ML engineers who want to move into the LLM and generative AI space
AI enthusiasts with programming knowledge who want to build real, deployable generative AI systems
Product managers and technical leads who want a deep understanding of what generative AI can and cannot do
Entrepreneurs and technical founders who want to build AI-native products and startups
Anyone with a programming background who wants to enter one of the highest-paying and fastest-growing fields in technology

Career Opportunities

Upon completing this course you will be prepared for roles such as:

Generative AI Engineer and LLM Application Developer
Prompt Engineer and AI Systems Designer
AI/ML Engineer with Generative AI Specialisation
RAG Pipeline Developer and Knowledge Base AI Engineer
AI Product Developer and AI Solutions Architect
Fine-Tuning and Model Customisation Specialist
AI Automation and Intelligent Agent Developer
Freelance AI Developer and Generative AI Consultant

Learning Mode

Hybrid Learning: Attend classes physically at our campus or join online via live interactive sessions.

Live Classes: Monday to Friday, 9:00 AM – 12:00 PM
Recorded Sessions: Access all class recordings anytime for revision at your own pace
Online Support: Get help via WhatsApp community and scheduled mentoring sessions
Practical Labs: Hands-on AI development sessions every week — live coding with LLM APIs, RAG pipeline construction, fine-tuning runs, and agent building with instructor guidance
Project Reviews: Structured technical code reviews and AI system feedback at every project milestone throughout the program
AI Community Access: Join our generative AI developer community for peer collaboration, prompt engineering challenges, hackathon participation, and exposure to live AI industry job opportunities

Whether you are a developer ready to ride the biggest wave in technology, an AI enthusiast who wants to go beyond using AI tools to actually building them, or a professional who wants to future-proof their career — this 3-month program gives you the technical depth, hands-on project experience, and industry recognition to succeed as a Certified Generative AI Professional.

What You Will Learn

Understand the architecture and capabilities of large language models including GPT-4o, Claude, Gemini, Llama 3, and Mistral

Engineer advanced prompts using chain-of-thought, few-shot, role prompting, and structured output techniques

Build Retrieval-Augmented Generation (RAG) pipelines with LangChain, vector databases, and document ingestion workflows

Integrate OpenAI, Anthropic, and open-source LLM APIs into Python applications and FastAPI backends

Fine-tune open-source LLMs on custom datasets using LoRA and QLoRA parameter-efficient training techniques

Generate and manipulate images using Stable Diffusion with ControlNet, inpainting, and prompt-based style control

Build autonomous AI agents with tool use, memory, and multi-step reasoning using LangChain Agents and AutoGen

Design and implement production-grade AI applications with Streamlit frontends and Docker containerised deployments

Evaluate LLM outputs using BLEU, ROUGE, and LLM-as-judge evaluation frameworks for quality assurance

Apply AI safety, ethical AI principles, prompt injection defences, and responsible deployment practices to production systems

Course Curriculum

Month 1 – LLM Foundations, Prompt Engineering & OpenAI API

5 lessons

Weeks 1–2

Generative AI Landscape: LLM Architecture, Transformer Models, Tokenisation, and the State of the Industry
OpenAI API Deep Dive: Authentication, Chat Completions, System Prompts, Temperature, and Token Management
Advanced Prompt Engineering: Zero-Shot, Few-Shot, Chain-of-Thought, ReAct, and Structured Output Techniques
OpenAI Function Calling and JSON Mode: Extracting Structured Data and Tool Use from LLM Responses
Multimodal AI with GPT-4 Vision and DALL-E 3: Image Understanding, Image Generation, and Whisper Speech-to-Text

Month 1 – Multi-Provider LLMs, Embeddings & Vector Databases

5 lessons

Weeks 3–4

Anthropic Claude and Google Gemini APIs: Integration Patterns, Strengths, and Multi-Provider Strategy
Open-Source LLMs with Hugging Face: Loading Llama 3, Mistral, and Phi-3 for Local and Cloud Inference
Text Embeddings Explained: Semantic Search, Cosine Similarity, and Embedding Models from OpenAI and Hugging Face
Vector Databases: ChromaDB, Pinecone, and FAISS — Storing, Indexing, and Querying Embeddings at Scale
Mini Project: Semantic Document Search Engine — Embed a Knowledge Base and Build a Similarity Search API

Month 2 – RAG Pipelines, LangChain & LLM Application Development

5 lessons

Weeks 5–6

LangChain Framework: Chains, Prompts, Memory, and Output Parsers for Structured LLM Application Development
Retrieval-Augmented Generation (RAG): Document Loaders, Text Splitters, Retrieval Chains, and Source Citation
Advanced RAG Techniques: Hybrid Search, Re-ranking, Query Transformation, and Multi-Query Retrieval Strategies
LlamaIndex for Document QA: Index Types, Query Engines, and Integrating Knowledge Graphs into LLM Applications
Mini Project: Custom AI Chatbot with RAG — Domain-Specific QA System with ChromaDB, LangChain, and Streamlit UI

Month 2 – LLM Fine-Tuning & Model Customisation

5 lessons

Weeks 7–8

Fine-Tuning vs RAG vs Prompt Engineering: When to Use Each Approach and Trade-offs in Production Systems
Hugging Face PEFT Library: LoRA and QLoRA for Parameter-Efficient Fine-Tuning on Consumer and Cloud Hardware
Dataset Preparation for Fine-Tuning: Data Collection, Cleaning, Formatting (Alpaca, ChatML), and Quality Assessment
Training and Evaluating a Fine-Tuned LLM: Training Loop, Loss Monitoring, BLEU/ROUGE Metrics, and LLM-as-Judge
Mini Project: Fine-Tuned Domain-Specific LLM — Train Llama 3 or Mistral on a Custom Dataset and Deploy to Hugging Face Spaces

Month 3 – Image Generation, AI Agents & Autonomous Systems

5 lessons

Weeks 9–10

Stable Diffusion Architecture: Latent Diffusion Models, UNet, VAE, CLIP, and the Image Generation Pipeline
Stable Diffusion Workflows: txt2img, img2img, Inpainting, ControlNet Conditioning, and LoRA Style Fine-Tuning
LangChain Agents: Tool Use, ReAct Loop, Web Search, Code Execution, and Building Goal-Directed AI Agents
AutoGen Multi-Agent Systems: Designing Agent Networks for Complex, Multi-Step Enterprise Automation Tasks
Mini Project: Autonomous Research Agent — AI Agent that Searches, Reads, Synthesises, and Reports on Any Topic

Month 3 – AI Safety, Production Deployment & Capstone Project

5 lessons

Weeks 11–12

AI Safety and Ethics: Prompt Injection Attacks, Jailbreaking, Bias, Hallucination, and Responsible AI Deployment
Building AI APIs with FastAPI: Wrapping LLM Pipelines into Secure, Scalable REST Endpoints for Production Use
Containerising AI Applications with Docker: Building Images, Managing Dependencies, and Deploying to Cloud Services
AI Application Monitoring: Logging, LLM Observability with LangSmith, Error Handling, and Cost Optimisation
Capstone Project: Full Production Generative AI Application — Design, Build, Deploy, and Document an End-to-End AI System

4.9

Duration3 Months
LevelFoundation Course
ScheduleMonday – Friday
ModeHybrid (Physical & Online)
CertificateProfessional Certificate upon completion
SupportLifetime access to alumni network and resources
Admission FeeRs. 15,000
Monthly FeeRs. 10,000

Admission Note: Admission fee includes the first month's tuition fee. You only need to pay Rs. 15,000 at the time of admission.

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