Generative Artificial Intelligence Phase II

Event Date: 04 May 2026 – 13 May 2026

Event Brief Description

The School of Artificial Intelligence (SoAI), Galgotias University, successfully organized a 20-hour Certification Program on Generative AI, NLP, and LLM Technologies from 04 May to 13 May 2026 at the NVIDIA Supercomputing Lab. The intensive certification programme was designed to equip participants with industry-relevant knowledge and practical skills in Generative Artificial Intelligence, Natural Language Processing (NLP), and Large Language Models (LLMs). Through hands-on coding sessions, live demonstrations, collaborative projects, and practical exercises, participants gained experience with advanced AI tools and frameworks including Hugging Face Transformers, LangChain, LangGraph, Gemini CLI, Blackbox AI, and Google Cloud Platform. The programme successfully bridged the gap between academic learning and modern AI industry practices while preparing students for emerging opportunities in Artificial Intelligence and Machine Learning.

Event Detailed Description

The School of Artificial Intelligence (SoAI), Galgotias University, organized the 20-hour Certification Program on Generative AI, NLP, and LLM Technologies from 04 May to 13 May 2026 at the NVIDIA Supercomputing Lab. The certification programme was designed to provide participants with comprehensive theoretical knowledge and practical exposure to modern Artificial Intelligence technologies, focusing on Generative AI, Natural Language Processing (NLP), and Large Language Models (LLMs).

The programme adopted a structured learning approach that progressed from the fundamentals of Artificial Intelligence and Natural Language Processing to advanced concepts including Transformer architectures, BERT, GPT models, prompt engineering, LangChain, LangGraph, Retrieval-Augmented Generation (RAG), and Agentic AI systems. Participants explored the evolution of AI models, beginning with Recurrent Neural Networks (RNNs), LSTMs, and GRUs before advancing to modern Transformer-based architectures that power today's intelligent AI systems.

A major highlight of the programme was its emphasis on experiential learning. Through guided coding sessions, live demonstrations, and supervised practical exercises, participants worked with leading AI development frameworks and platforms such as PyTorch, TensorFlow, Hugging Face Transformers, Gemini CLI, Blackbox AI, LangSmith, and Google Cloud Platform. They also gained hands-on experience in prompt engineering techniques, vector databases, semantic search, AI workflow orchestration, and Retrieval-Augmented Generation (RAG), enabling them to build intelligent AI-powered applications.

Participants collaborated in teams to conceptualize, design, and develop real-world AI solutions, integrating Large Language Models with modern AI workflows and cloud-based deployment strategies. The collaborative project-based learning approach enhanced their programming, debugging, analytical thinking, and problem-solving abilities while providing practical exposure to current industry standards.

The certification programme concluded with a capstone project presentation and demonstration session where participant teams showcased their AI applications before faculty members and peers. Students explained their implementation methodologies, workflow architectures, technical challenges, and deployment strategies, strengthening both their technical proficiency and professional presentation skills.

The programme successfully created an industry-oriented learning ecosystem that enabled participants to develop strong conceptual understanding and practical competency in Generative AI technologies. It also reinforced Galgotias University's commitment to preparing future AI professionals through advanced, application-focused education aligned with global technological advancements.

Department Name: School of Artificial Intelligence (SoAI)

Event Outcome

The certification programme successfully enhanced participants' knowledge and practical competencies in Generative Artificial Intelligence, Natural Language Processing, and Large Language Model technologies. Students gained hands-on experience with modern AI frameworks, prompt engineering, Retrieval-Augmented Generation (RAG), vector databases, AI workflow orchestration, and cloud-based deployment platforms. Through collaborative project development and practical implementation, participants strengthened their programming, debugging, communication, and problem-solving skills while gaining exposure to industry-oriented AI development practices. The programme successfully prepared students for emerging opportunities in Artificial Intelligence, Machine Learning, NLP, and intelligent software development through an experiential and application-driven learning approach.

Related Goal