GenAI for Developers

Generative AI creates numerous new opportunities for companies, especially when it comes to increasing productivity and efficiency. It makes features cheaper or even feasible in the first place. However, as these models are not deterministic, they occasionally provide incorrect or unusable information. In this workshop, we will shed light on the properties of generative models and understand the causes of these challenges. We will learn how to feed the models with our own data in order to improve the results. We will use the Retrieval Augmented Generation (RAG) architecture.

The aim of the course is to get to know all the building blocks of a simple RAG architecture. The experiments are designed to introduce these building blocks step by step. At the end of the workshop, we will combine all components into a chatbot that works with our own documents.

Day 1

• Fundamentals of Large Language Models (LLMs)
  • Architecture
  • Model parameters
  • Prompting
• Working with the OpenAI Chat API
• Building a simple LLM chatbot
• First experiments with the chatbot

Day 2

• Function Calling: The LLM as an agent
  • Implementing your own functions for the LLM
• Basics of embeddings
  • Semantic representation with vectors
  • Determining text similarity
• Retrieval Augmented Generation (RAG)
  • Use cases
  • Preparing company data for LLMs
  • Vector databases
  • Make company data available for the chatbot
• Test LLMs
  • When are generated answers “good”?
  • Use embeddings to create test scenarios
  • Use user feedback to improve the answers