The course is designed for companies that want to understand and implement Generative Artificial Intelligence (AI) technologies in their work environment. Starting from the basics of prompt engineering, the course delves into the use of language models (LLM), the integration of business knowledge through the RAG (Retrieval-Augmented Generation) paradigm, and the use of AI Agents to automate processes and generate new monetization opportunities.
CONTENT
The course explores the following topics:
Introduction to Generative Artificial Intelligence:
• Context and impact of AI in the business world.
• Historical evolution and fundamental concepts.
In-depth Study of LLMs:
• Definition, architecture and functioning.
• Prompt engineering techniques and practical applications.
• Limitations and ethical aspects.
RAG (Retrieval Augmented Generation):
• Principles and advantages of the RAG paradigm.
• Techniques for collecting, indexing and integrating business knowledge.
• Workflow and operational tools.
AI Agents for Automation:
• Definition and role of AI Agents.
• Applications in business process automation.
• Monetization strategies and case studies.
Interactive Workshop and Case Studies:
• Analysis of real scenarios and best practices.
• Q&A session and brainstorming for customizing AI solutions.
OBJECTIVES
The course aims to:
• Provide a comprehensive understanding of the fundamentals of Artificial Intelligence and its impact on business.
• Illustrate the potential and practical applications of language models (LLM) in the business context.
• Show how to leverage the RAG paradigm to integrate and enhance internal knowledge.
• Demonstrate how to implement AI Agents for process automation and generation of new revenue streams.
• Stimulate a strategic vision to guide digital transformation in the company.
LEARNING OUTCOMES
Upon completion of the course, participants will be able to:
• Understand the basic principles of Generative Artificial Intelligence, advantages, limitations and the role it can play in business strategies.
• Effectively use LLMs to generate content and support textual analysis.
• Understand RAG techniques to leverage business knowledge and improve decision-making processes.
• Understand and design automation solutions based on AI Agents.
• Evaluate ethical, security and scalability aspects in the adoption of AI technologies.