IT Business Analysis with AI and Large Language Models - Extended Version

• For IT specialists who are considering retraining for the role of IT Business Analyst,
• for IT Business Analysts, Business Architects, or Product Managers who aim to enhance their qualifications in the analytical craft,
• and generally for all roles involved in IT or Business Analysis and preparing requirements for developers, both on projects and in agile teams.

The goal of the course is to guide participants through the entire process of IT Business Analysis and its main techniques, focusing on the practical use of large language models like ChatGPT and generative AI in various analytical activities.
Participants will have the opportunity to familiarize themselves with the capabilities of existing AI technologies. During the exercises, they will try practical techniques and methods that will increase productivity and efficiency in analysis.

Summary of IT Business Analysis Procedures

• Enterprise Analysis: Motivation, stakeholder analysis, process modeling
• Working with Requirements: Elicitation, documentation, and prioritization of requirements
• Functional Specification Tools: Use-case modeling, UX prototypes
• Agile Analysis Techniques: Use stories, working with the backlog, product approach
  

Introduction to the capabilities of large language models like ChatGPT and other generative tools

• Working with Text
• Working with Diagrams
• Working with Data
• Working with Code/Scripts
• Integration with Other Tools
  

Prompt Engineering Basics for Analysts

• How large language models work
• Best practices for writing prompts
• Examples of effective prompts
• Bots vs Agents
  

Practical Exercise: IT Business Analysis using AI from business requirements to functional design, such as:

• Project/meeting preparation – initial research
• Preparation of templates, questionnaires, and other tools for effective work and professional methodology
• Working with information: generating questions, finding alternatives or inconsistencies
• Creating drafts of textual and graphical outputs (processes, needs, requirements, user stories, use cases, UX prototypes, UML diagrams, ...)
• Improving and quality assurance of outputs
• Safe use of AI tools, i.e., what to be cautious about and what to avoid
  

Advanced Techniques and Future Trends

• Creating CustomGPTs tailored for your team or project
• The RAG technique and how to use it for project/product-specific conversations
• How to launch and develop local language models for an additional level of security