A high‐level, engineering‐oriented course designed to empower professionals with the theory and hands‐on skills needed to embed Generative AI solutions in real‐world pipelines.
Discover MoreThe roadmap to becoming a GenAI‐savvy professional in business contexts.
This GenAI for Business course (10 CFU, entirely in English) provides participants with both the theoretical foundations and practical toolset to design, implement, and optimize Generative AI systems tailored to corporate needs. From Natural Language Processing and knowledge representation, to retrieval‐augmented pipelines and reinforcement learning strategies (RLHF), you will learn how to: integrate LLMs, build chatbots, harness RAG, structure corporate knowledge with ontologies, and develop AI‐enabled decision support systems. Organized in an online format with Saturday lectures (9:00–18:00) from November 22, 2025 to January 24, 2026, the course demands 70% attendance and culminates in a written final exam. Perfect for professionals seeking a cutting‐edge, high‐impact profile in the AI‐driven era.
Key Details | |
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Course Period | Nov 22, 2025 – Jan 24, 2026 |
Format | Online (Saturdays, 9AM–6PM) |
Language | English |
Credits (CFU) | 10 CFU |
Total Hours | 80 hrs |
Additional Details | |
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Attendance |
70%
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Cost | €1,890 (in two installments: €1,200 + €690) |
Location | online |
Enrollment Deadline | 27 October 2025 |
What you will be able to achieve by the end of this course.
Master advanced NLP techniques (lexical representations, word/contextual embeddings, transformer‐based LLM architectures), knowledge representation (ontologies, semantic graphs), and RAG systems. Grasp Reinforcement Learning fundamentals, including RLHF, policy learning, and strategic evaluation.
Design and implement integrated GenAI pipelines for chatbots and virtual assistants. Leverage knowledge bases and RAG frameworks to boost response relevance. Evaluate performance via structured benchmarks and public datasets, ensuring robust validation of your conversational systems.
Cultivate critical thinking to assess AI solutions in real‐world industrial contexts. Balance ethical, technical, and regulatory considerations to guarantee transparency, reliability, and continuous improvement of GenAI systems.
Translate complex AI concepts for diverse stakeholders. Develop written and oral skills through presentations and collaborative projects, facilitating evidence‐based decision‐making and cross‐departmental collaboration.
Build autonomous, lifelong learning capabilities with open‐source AI tools. Execute hands‐on projects that foster problem‐solving, teamwork, and creative thinking. Merge IR and RAG techniques with extraction methods to generate strategic corporate insights.
Meet the Course Director, Vice-Director, and internal organizers
Gianmaria Silvello
Department of Information Engineering (DEI),
Università degli Studi di Padova
049 827 7932
Giorgio Satta
Department of Information Engineering (DEI),
Università degli Studi di Padova
049 827 7948
Professors and researchers from Università di Padova
Name | Role / Qualifica | Affiliation |
---|---|---|
Giorgio Maria Di Nunzio | Associate Professor | Università degli Studi di Padova |
Nicola Ferro | Full Professor | Università degli Studi di Padova |
Giorgio Satta | Full Professor | Università degli Studi di Padova |
Gianmaria Silvello | Full Professor | Università degli Studi di Padova |
Gian Antonio Susto | Associate Professor | Università degli Studi di Padova |
An in‐depth look at each teaching block.
# | Area/Thematic | Title | Instructor | CFU |
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1 | Machine Learning & Conversational Systems |
Natural Language Processing |
Prof. Giorgio Satta | 2 |
2 | Knowledge Representation |
Knowledge Representation & Management |
Prof. Gianmaria Silvello | 2 |
3 | Information Extraction & Access |
Conversational Systems & Evaluation |
Prof. Nicola Ferro | 2 |
4 | Information Extraction & Access |
Information Extraction |
Prof. Giorgio Maria Di Nunzio | 1 |
6 | Information Extraction & Access |
Privacy in the Use of GenAI |
Dr. Guglielmo Faggioli | 1 |
7 | Machine Learning & Conversational |
Reinforcement Learning |
Prof. Gian Antonio Susto | 1 |
8 | Machine Learning & Conversational |
Hands‐on Chatbot Development |
Dr. Chiara Masiero | 1 |
Three core pillars around which the course content is organized.
The Natural Language Processing (NLP) module provides the foundational skills to build advanced chatbots. Participants learn techniques such as lexical representation, word embeddings, language modeling, and sequence recognition/classification. By mastering neural network architectures (notably transformers), students design and optimize end-to-end NLP pipelines—enabling chatbots that understand and generate natural language with high accuracy. Hands-on work with open-source tools covers question-answering, information extraction, and virtual assistant development, powering more natural, context-aware conversational agents.
Knowledge modeling methodologies—using knowledge bases (e.g. RDF) and ontologies—are essential for any company leveraging Generative AI. These technologies let you semantically structure, integrate, and query large datasets, improving the accuracy and relevance of AI-generated responses in business processes. By building interoperable, scalable data models, students learn to maintain competitive advantages through organized knowledge graphs that fuel retrieval-augmented pipelines.
Information retrieval (IR) and extraction techniques are critical for fusing Generative AI with real-world data. Modules cover indexing, retrieval models, ranking, and retrieval-augmented generation (RAG), enabling systems to fetch highly relevant documents from large corpora. Participants also explore unsupervised/self-supervised extraction methods that uncover hidden patterns in unstructured text. Integrating these approaches ensures that AI solutions deliver accurate, contextually appropriate information in enterprise applications.
How to apply, requirements, deadlines and available seats.
To join Generative AI for Business, candidates must meet the following criteria and follow the application procedure:
How your progress is evaluated and the final certification details.
Assessment Details | |
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Final Exam | Written exam; minimum pass mark: 55/100. |
Ongoing Verification | Periodic quizzes and assignments to monitor your learning progress. |
Certification | Certificate issued upon passing the final exam and achieving at least 70% attendance. |
Meet the professors and experts leading this GenAI course.
Database Theory
Privacy Preservation
Information Retrieval, Evaluation and Conversational Search
Senior Data Scientist
Natural Language Processing
Knowledge Representation & Management
Reinforcement Learning
What Generative AI can achieve.
In today’s fast‐moving digital landscape, businesses must leverage Generative AI to stay competitive, drive innovation, and unlock new opportunities. From automating content creation and accelerating research to powering hyper-personalized customer experiences, Generative AI transforms raw data into actionable insights at unprecedented scale. Whether you’re a product manager, data scientist, or executive, understanding how these models generate human‐like text, design bespoke visuals, and adapt to evolving business needs is critical. The embedded Lottie animation isn’t just decoration—it brings the core concept to life, illustrating in real time how neural models, knowledge graphs, and retrieval systems converge to deliver dynamic, context-aware solutions.