Prompt Engineering -
or, how to ChatGPT With the Best of 'Em


When OpenAI released ChatGPT in November 2022, it instantly captured the public's imagination with its ability to answer questions, write poetry and riff on almost any topic. ChatGPT is driven by what A.I. researchers call a neural network. A neural network learns skills by analyzing data and pinpointing patterns in order to perform some task. Researchers at labs like OpenAI have designed neural networks that analyze vast amounts of digital text from many varied sources on a variety of topics. ChatGPT is just one example of a technology class referred to as Large Language Models. ChatGPT technology can appear amazing but can also blend fact with fiction and even make up information, a phenomenon that technologists refer to (by analogy) as "hallucination", while not informing you of those fictive limitations when they occur and are presented in ChatGPT's output or "completion".
Prompt engineering is the process of formulating a prompt for an AI system so that it produces a completion that closely matches your needs and expectations.



Documentation:
ChatGPT Cheat Sheets
Patterns for Prompt Engineering - Vanderbilt University
Prompt Engineering techniques by Lance Eliot
Chatbots Talk About Their Technological Underpinnings.pdf
A Prompt Pattern Catalog to Enhance Prompt Engineering with ChatGPT
Who Owns the Generative AI Platform
Copyright and Artificial Intelligence
BloombergGPT - A Large Language Model for Finance
Chain-of-Thought Prompting Elicits Reasoning
ChatGPT Prompt Patterns for Improving Code Quality, Refactoring, Requirements Elicitation, and Software Design
A Compact Guide to Large Language Models
Elon Musk and Larry Page's role in AI History - NYT, Dec-2023
Large Language Models - EdX course references, Extended General Definitions and History
AI And The Limits Of Language
What Is ChatGPT Doing and Why Does It Work - Stephen Wolfram.pdf
What's really behind chatbots and AI - Harvard's class notes for Introduction to Artificial Intelligence and ... supporting slides
Overview of Catastrophic AI, Sep2023
Frontier AI Regulation, Sep2023

----------- Chatbot Links -----------
ChatGPT (OpenAI)
Claude 2 (Anthropic)
Bard (Google)
Forefront.ai (added Oct-2023)
Chatbot Comparisons
Bing (Microsoft)
Falcon 180b (Technology Innovation Institute)

----------- External Video Links -----------
Objective-Driven AI by Prof. Yann LeCun (NYU/Meta), Aug 2023
The Impact of chatGPT, MIT Physics Dept, Dr Stephen Wolfram (Wolfram Research), Aug 2023
New Horizons in Generative AI: Science, Creativity, and Society, 24Oct2023 (8.5 hr length, but skimming is easy)

<-- Video control (left) plays ALL lessons, in sequence, and then loops back to beginning ... or alternatively,
select a link below to start a different lesson:


The Prompt Engineering videos above were sourced primarily (but not exclusively) from:
(1)Prompt Engineering and Advanced ChatGPT, offered by EdX, (other personal attributions not available);
(2) Prompt Engineering for ChatGPT, taught by Dr. Jules White, Associate Professor of Computer Science at Vanderbuilt University;
(3) Generative AI with Large Language Models, from DeepLearning.AI and AWS, taught by Chris Fregly, Instructor and Principal Solutions Architect, Generative AI, Amazon Web Services (AWS)