Prompt Engineering Training

Infoshare is the largest tech community in CEE and the organizer of the leading tech conference in Gdańsk. It connects startups, investors, corporations, and innovation enthusiasts. It promotes entrepreneurship, knowledge sharing, and networking. Through events, competitions, and programs, it supports the development of the tech ecosystem in Poland and the region.

The training includes an introduction to artificial intelligence, machine learning, language models, and tools such as ChatGPT and Copilot. Participants will learn about the construction and parameterization of ChatGPT, as well as best practices for creating prompts. We will also discuss practical aspects of working with Copilot, including creating functions and unit tests. This training provides a compact introduction to key aspects of AI and programming using advanced tools.

• The training is dedicated to beginner and intermediate programmers who want to expand their tech stack with tools that automate work and accelerate the acquisition of new programming techniques.

During the training, you will be introduced to the world of artificial intelligence and learn how to use highly advanced AI-based tools. You will gain the knowledge necessary to accelerate a programmer's work and reach a previously unattainable level in solving programming problems.

1. INTRODUCTION

2. DEFINITION OF AI AND MACHINE LEARNING
    • What is AI?
    • What is Machine Learning?
    • What is Deep Learning?

3. TYPES OF MACHINE LEARNING
    • Supervised Learning
    • Unsupervised Learning
    • Reinforcement Learning

4. NLP

5. COMPUTER VISION

6. ORIGIN AND TYPES OF LARGE LANGUAGE MODELS
    • What is a Large Language Model?
    • How do large language models differ from "normal" ones?
    • What are the types and families of large language models?

7. WORKING ENVIRONMENT, CHATGPT, COPILOT
    • How to launch ChatGPT?
    • What do we get in the free and paid plan?
    • Which Copilot plan to choose?
    • Configuration of Copilot and ChatGPT

8. BUILDING A GPT MODEL
    • Components of GPT
    • Training the GPT model
    • Types of GPT models

9. HOW IT FUNCTIONS, IN/OUT
    • How does GPT process data?
    • What output can we get?

10. PARAMETERIZATION
     • What can we achieve by adjusting parameters?
     • Temperature
     • Cutoff
     • Response length

11. LIMITATIONS
     • Length of prompt along with the response
     • Knowledge range
     • Hallucinations

12. PROMPT ENGINEERING FOR DEV WITH CHATGPT
     • Best practices
     • Zero-shot prompting
     • Few-shot prompting
     • Chain-of-thought prompting
     • Role prompting
     • Building simple functions
     • Building classes
     • Writing unit tests
     • Code review
     • Writing documentation

13. COPILOT
     • Configuration in IDE
     • Basic functionality
     • Building simple functions
     • Building classes
     • Writing unit tests
     • Working with multiple sources

16 h/2 days