LPU - Lovely Professional University
What you'll learn

This summer training program is designed to provide students, researchers, and professionals with practical knowledge of Python programming integrated with the latest advancements in Generative Artificial Intelligence (GenAI). The course focuses on building intelligent AI-powered applications using Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), prompt engineering, AI automation workflows, and modern Python frameworks. Participants will begin with Python fundamentals and gradually explore advanced AI concepts including OpenAI APIs, LangChain, vector databases, embeddings, AI agents, document-based chatbots, and workflow automation. The training emphasizes hands-on implementation through real-world projects such as AI assistants, automated content generators, smart document analyzers, and RAG-enabled applications. The program also introduces integration of GenAI with automation tools for improving productivity in business, research, education, and software development. By the end of the training, participants will be able to design, develop, and deploy AI-driven solutions using Python and modern Generative AI technologies.

  • This module introduces learners to Generative AI with Python, covering Python fundamentals, APIs, automation workflows, prompt engineering, and practical AI applications. Participants gain hands-on experience through labs, reusable utilities, data handling, API integration, and AI text generation projects. The training concludes with a mini project focused on building real-world AI-powered productivity tools.
  • This module covers LLM fundamentals, prompt behavior, model parameters, API integration, structured AI outputs, embeddings, semantic search, and vector databases using ChromaDB. Participants gain practical experience in building reusable GenAI services, validating JSON outputs, creating embeddings, and developing local knowledge-base search systems for real-world AI applications.
  • This module focuses on Retrieval-Augmented Generation (RAG), document ingestion, chatbot development, vector databases, and GenAI application deployment. Participants learn retrieval pipelines, context handling, Streamlit-based UI development, and FastAPI integration for AI services. Hands-on labs guide learners in building RAG chatbots, APIs, and interactive AI-powered knowledge assistants
  • This module introduces workflow automation, AI-powered lead management, and campaign content generation using Python and modern automation platforms. Participants learn trigger-action workflows, form and email automation, webhook integration with n8n/Zapier/Power Automate, lead classification techniques, and AI-driven content creation. Hands-on labs focus on real-world automation, enquiry handling, and marketing workflows.
  • The capstone project workflow covers planning, system architecture, data design, AI integration, RAG implementation, automation, and reporting. Students develop input systems, integrate Generative AI models, create knowledge retrieval pipelines, automate notifications and dashboards, and finally perform testing, debugging, documentation, and demo rehearsal to ensure successful project execution and presentation readiness.
  • These AI-based solutions enhance academic management, student support, communication, and project monitoring through intelligent automation. The systems use Generative AI, RAG, analytics, and workflow tools to manage enquiries, answer document-based queries, recommend projects, assess placement readiness, generate promotional content, and track internships or projects with automated feedback and performance summaries.

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Kamalpreet Kaur Bagral
Assistant Professor

Ms. Kamalpreet Kaur is an Assistant Professor at Lovely Professional University, Phagwara, India, with over 13 years of academic and research experience. She earned her Ph.D. from Dr. B.R. Ambedkar National Institute of Technology Jalandhar and has developed strong expertise in Machine Learning, Network Security, Artificial Intelligence, and Intelligent Systems. Her research interests include secure communication protocols, AI-driven healthcare applications, and applied data science for real-world problem solving. She has published more than 20 research papers in reputed SCI and Scopus-indexed journals and conferences, along with contributions to book chapters and patents. Her research work has received recognition through multiple Best Paper Awards, and she has delivered keynote talks at various international conferences and faculty development programs. She has also been associated with government-funded and interdisciplinary research projects, including collaborations linked with organizations such as ISRO. Ms. Kaur actively contributes to academic and innovation activities as a reviewer, session chair, mentor, and organizing committee member for national and international conferences, workshops, FDPs, and hackathons. She has also served as a jury member in innovation initiatives such as the Smart India Hackathon. Dedicated to student mentorship and research excellence, she continues to guide undergraduate and postgraduate students in emerging technologies and impactful interdisciplinary research.