Overview

In this tutorial, participants will explore the power of object-oriented programming (OOP) in interfacing with large language models (LLMs). We will begin by providing a brief overview of LLMs. Then, we will delve into the fundamental principles of OOP, including encapsulation, inheritance, and polymorphism, and discuss how these concepts can be applied to facilitate seamless interactions with LLMs.

Through hands-on coding exercises, attendees will learn how to design and implement object-oriented solutions for tasks such as text generation, and entity extraction using LLMs.

By the end of the tutorial, participants will have gained a comprehensive understanding of how OOP can be leveraged to effectively interface with LLMs, resulting in more efficient and maintainable codebases.

Outline (1 hour)

1. Introduction and Setup (5 Minutes)

2. Introduction to Large Language Models (LLMs) (5 minutes)

3. Overview of LLMs and their capabilities

4. Why Object-Oriented Programming for LLMs (5 minutes)

   • Modularity and code reusability
   • Encapsulation of LLM-specific logic
   • Easier integration with existing codebases
   • Scalability and extensibility
   • Ability to switch between different LLM providers

5. Designing an LLM Interface using OOP Approach (10 minutes)

   • Designing classes for LLM interaction
   • Encapsulating LLM functionality
   • Leveraging inheritance and polymorphism

6. Hands-on Coding Exercise (30 minutes)

   • Text generation using LLMs with OOP in Python

7. Q&A and Additional Discussion (5 minutes)

Prerequisite for participants

1. Basic understanding of Python programming
2. Familiarity with Google CoLab (or Kaggle or a local development setup with a Jupyter notebook will also suffix with Python >=3.6, pip3)
3. Cloudflare Worker AI Account (or any LLM as a Service platform of their choice)