Skip to content

Latest commit

 

History

History
62 lines (47 loc) · 4.15 KB

README.md

File metadata and controls

62 lines (47 loc) · 4.15 KB
Raw

What The Hack - Azure OpenAI Fundamentals

Introduction

The Azure OpenAI Fundamentals What The Hack is an introduction to understanding the conceptual foundations of Azure OpenAI models. Materials from this hack can serve as a foundation for building your own solution with Azure OpenAI.

This hack consists of five challenges and is designed to be self-administered, so anyone can complete the material independently. Whether you have limited to no experience with Machine Learning or have experimented with OpenAI before but want a deeper understanding of how to implement an AI solution, this hack is for you.

What The Hack is normally hosted as a 1-3 day event and is a team based activity where students work in groups of 3-5 people to solve the challenges. While this hack has been designed to be self-administered and completed self-paced, we still encourage you to pull in a friend or two to work with and discuss your learnings.

Learning Objectives

This hack is for anyone who wants to gain hands-on experience experimenting with prompt engineering and machine learning best practices, and apply them to generate effective responses from ChatGPT and OpenAI models.

Participants will learn how to:

  • Compare OpenAI models and choose the best one for a scenario
  • Use prompt engineering techniques on complex tasks
  • Manage large amounts of data within token limits, including the use of chunking and chaining techniques
  • Grounding models to avoid hallucinations or false information
  • Implement embeddings using search retrieval techniques

Evaluate models for truthfulness and monitor for PII detection in model interactions

Challenges

  • Challenge 00: Prerequisites - Ready, Set, GO!
    • Prepare your workstation to work with Azure.
  • Challenge 01: Prompt Engineering
    • What's possible through Prompt Engineering
    • Best practices when using OpenAI text and chat models
  • Challenge 02: OpenAI Models & Capabilities
    • What are the capacities of each Azure OpenAI model?
    • How to select the right model for your application
  • Challenge 03: Grounding, Chunking, and Embedding
    • Why is grounding important and how can you ground a Large Language Model (LLM)?
    • What is a token limit? How can you deal with token limits? What are techniques of chunking?
  • Challenge 04: Retrieval Augmented Generation (RAG)
    • How do we create ChatGPT-like experiences on Enterprise data? In other words, how do we "ground" powerful LLMs to primarily our own data?
  • Challenge 05: Responsible AI
    • What are services and tools to identify and evaluate harms and data leakage in LLMs?
    • What are ways to evaluate truthfulness and reduce hallucinations? What are methods to evaluate a model if you don't have a ground truth dataset for comparison?

Prerequisites

Contributors