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Artificial Intelligence is no longer a future concept—it is a present-day business driver transforming how organizations operate, compete, and innovate. From predictive analytics to intelligent automation and breakthrough generative models, AI is reshaping industries at an unprecedented pace. The rise of generative AI tools powered by models such as OpenAI’s GPT systems and Google DeepMind’s advanced architectures has accelerated this transformation, making AI more accessible, powerful, and practical than ever before.
This Two-Day AI / Generative AI Awareness & Advanced Certification Course is designed to provide participants with both foundational understanding and advanced practical knowledge of AI and Generative AI technologies. The program bridges the gap between conceptual awareness and real-world implementation, enabling professionals to confidently evaluate, adopt, and govern AI solutions within their organizations.
This course is ideal for business leaders, managers, technology professionals, entrepreneurs, consultants, and decision-makers seeking to stay competitive in an AI-driven economy. By the end of the program, participants will not only grasp the transformative potential of AI but also acquire the practical skills and strategic mindset required to lead AI initiatives responsibly and effectively.
Contents:
| AI/Gen AI Awareness | DAY 2 – Gen AI Advanced |
| Objectives: Impart good insights into Gen AI technology, capability & tools with hands-on experience for productivity and innovation.
Module 1: Introduction to AI · AI Landscape & Technologies · Differentiate Between AI/ML · AI Use Cases · AI Myths Module 2: Generative AI, LLMs, Tools & Security Considerations · Overview of LLMs · Gen AI Capability Demo · Data Security & Ethical AI Module 3: Prompt Engineering · Prompt Engineering Techniques & Demo Module 4: Functional Use Cases · Gen AI Use Cases Across various functions: HR, Ops, Marketing, Finance, Legal etc. Module 5: Productivity & Innovation Tools · Gen AI for Workplace Productivity & Innovation. Various multi-Media Tools Module 6: Future Trends · Emerging Trends in Gen AI · Q/A & Wrap-Up
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Objectives: Covers advanced techniques like LLM API Integration, RAG, Fine tuning
Session 1: LLM Internals & APIs · How large language models work (tokenization, attention, layers) · What is a transformer in plain terms · Prompt context window & token limits · LLM Fine-tuning Session 2: Embeddings & Vector Search · What are text embeddings (in simple terms) · Why and how we convert text to vectors · Cosine similarity and nearest neighbour matching · Overview of vector database like FAISS/Chroma Session 3: Retrieval-Augmented Generation (RAG) · What is RAG (retriever + generator) · Difference between RAG and fine-tuning · RAG architecture in simple blocks · Use cases: document search, chatbots, FAQ assistants Session 4: Capstone Project: Build a GenAI-Powered App · Designing a practical GenAI app · GenAI app components: data, LLM, UI, evaluation · Tips for improving quality (context selection, reranking) · Evaluation basics (grounding, relevance) · Q/A & Wrap-Up |
Trainer Profile: Aman Khajanchi, over the last three decades, had the opportunity to work across some of the world’s most respected technology companies—Microsoft, Oracle, HP, and HCL. His career has spanned the full spectrum of IT and consulting sales, helping clients make confident technology decisions during times of transformation. This long journey has given me a front-row seat to how digital tools evolve, how businesses adapt, and most importantly, how people respond when change arrives at their doorstep. Corporate AI Enablement Leader and GenAI Learning Architect Today. His sessions are grounded in real-life business conversations. He uses simple, relatable analogies—often drawn from daily life—to demystify complex topics like Generative AI and Agentic AI. His strength lies in connecting dots across functions and helping business teams see not just how a tool works, but how it fits into their world and their goals. Complementing this, He has been a certified data privacy professional and consultant since 2016, having worked closely with teams on responsible data practices. This background adds depth to AI work—ensuring that innovation is always balanced with governance, ethics, and trust.
