Generative AI for Business:
From Basics to Breakthroughs

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.

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Course Overview

The 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
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
Attendance
70%
Cost €1,890 (in two installments: €1,200 + €690)
Location online
Enrollment Deadline 27 October 2025

Learning Objectives

What you will be able to achieve by the end of this course.

1. Knowledge & Comprehension

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.

2. Application of Knowledge

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.

3. Autonomous Judgment

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.

4. Communication Skills

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.

5. Learning & Innovation

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.

Management & Organizing Committee

Meet the Course Director, Vice-Director, and internal organizers

Course Director

Gianmaria Silvello

Department of Information Engineering (DEI),
Università degli Studi di Padova

049 827 7932

gianmaria.silvello@unipd.it

Vice-Director

Giorgio Satta

Department of Information Engineering (DEI),
Università degli Studi di Padova

049 827 7948

giorgio.satta@unipd.it

Internal Organizing Committee

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

Course Modules

An in‐depth look at each teaching block.

# Area/Thematic Title Instructor CFU
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

Thematic Areas

Three core pillars around which the course content is organized.

Machine Learning Icon

Machine Learning & Conversational Systems

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 Management Icon

Knowledge Representation & Management

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 Extraction Icon

Information Extraction & Access

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.

Admissions & Enrollment

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:

  • Eligibility: Bachelor Degree in any scientific subject (e.g. Engineering, Statistics, Mathematics, etc.)
  • Selection Procedure: No written/oral exam. Evaluation based on CV (max 70 pts), thesis (10 pts), publications (10 pts), other titles (10 pts); minimum pass mark 55/100.
  • Available Seats: Min 12 – Max 60 (plus 2 reserved seats for candidates with disabilities).
  • Application Deadline: 27 October 2025.
  • Apply Online: genai4biz.dei.unipd.it

Assessment & Certification

How your progress is evaluated and the final certification details.

Assessment Details
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.

Course Instructors

Meet the professors and experts leading this GenAI course.

Prof. Giorgio Maria Di Nunzio

Prof. Giorgio Maria Di Nunzio

Database Theory

Giorgio Maria Di Nunzio

  • Associate Professor, University of Padova
  • PhD in Computer Engineering, University of Padova
  • Research: Databases, Linguistics and Machine Learning
Dr. Guglielmo Faggioli

Dr. Guglielmo Faggioli

Privacy Preservation

Guglielmo Faggioli

  • Postdoctoral Researcher, University of Padova
  • PhD in Information Engineering, University of Padova
  • Research: Information Retrieval, Differential Privacy, IR Evaluation
  • Proceedings co-chair of CLEF
  • Best Resource Paper Award at CIKM 2024, Honorable Mention for Best Paper at SIGIR2023, Best Paper Award at ICTIR 2023, Best Paper Award at ECIR 2021
Prof. Nicola Ferro

Prof. Nicola Ferro

Information Retrieval, Evaluation and Conversational Search

Nicola Ferro

  • Full Professor, University of Padova
  • PhD in Computer Science, University of Padova
  • Research: Information Retrieval, Data Management and Representation, Evaluation
  • Chair of the Steering Committee of: CLEF, ESSIR, ACM AEC, IEEE TCDL
  • Chair of the Executive Committee of: ACM SIGIR
  • Winner of the 2024 UKeiG Strix Award
  • 250+ published papers
Dr. Chiara Masiero

Dr. Chiara Masiero

Senior Data Scientist

Chiara Masiero

  • PhD in Information Engineering, University of Padova
  • Senior Data Scientist and Lead of R&D @ Statwolf
  • LLM-Based Chatbots, Sentiment Analysis, Text classification
  • Collaborated with Shanghai Jiao Tong University, China
Prof. Giorgio Satta

Prof. Giorgio Satta

Natural Language Processing

Giorgio Satta

  • Full Professor, University of Padova
  • PhD in Computer Science, University of Padova
  • Post-Doctoral Experience at University of Pennsylvania, Philadelphia
  • Visiting scientist at Johns Hopkins University, Baltimore, and Paris Diderot University (Paris 7)
  • Research: Natural Language Processing and Computational Linguistics
  • Co-author of 160+ peer-reviewed papers
Prof. Gianmaria Silvello

Prof. Gianmaria Silvello

Knowledge Representation & Management

Gianmaria Silvello

  • Full Professor, University of Padova
  • PhD in Information Engineering, University of Padova
  • Post-Doctoral Experience at University of Pennsylvania, Philadelphia
  • Visiting researcher at University of Edinburgh
  • Research: Knowledge Graphs, Knowledge Representation and Management
  • Coordinator of the EU project HEREDITARY, WP leader in EU projects EXA-MODE and BRAINTEASER
Prof. Gian Antonio Susto

Prof. Gian Antonio Susto

Reinforcement Learning

Gian Antonio Susto

  • Associate Professor, University of Padua
  • PhD in Information Engineering, Post-Doctoral experience at University of Ireland
  • Collaborated with Infineon Technologies Austria, STMicroelectonics and Micron
  • Research: Reinforcement Learning, Deep Learning, Robotics
  • Chief Data Officier & Co-Founder @ Statwolf LTD

Generative AI in Action

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.