Mitigations like these are really for avoiding embarrassment. If it’s actually strategically important that your competitors don’t know what GPT-3 prompt you’re using in a public-facing app, you’re in trouble. You need to add value elsewhere.
PROMPT ENGINEERING
-
Defending Your Prompts from Leaks and Plagiarism
By
–
I keep seeing prompt leaks. Dozens, now. I know of one startup whose prompt is plagiarized from another’s that leaked — revealed when theirs also leaked. Defend your prompt. Regex-filter your generations and throw an error if they contain snippets of your instructions.
-
Prompt Engineering: Discover Inputs for Best AI Outputs
By
–
Prompt engineering helps
⦿ Discover the inputs to get the best generations / outputs for our problem ⦿ Provide problem context and settings for the given task
⦿ Give prior examples for the problem at hand – more like few shot learning Happy learning & happy holidays! -
Prompt Engineering Guide for LLMs Released by DAIR AI
By
–
With the advent of LLMs like GPT3, ChatGPT etc, Prompt Engineering is deserving more attention now than ever before. @dair_ai has released a “Prompt Engineering Guide” that contains the set of learning guides and tools about prompt engineering. Link:
-
LLMs like Copilot replace Google for code installation commands
By
–
Imports and installation commands are the purest instance of LLMs replacing Google for me. Only the comments in this screenshot were written by hand, and the rest is Copilot completion:
-
Stable Diffusion v3: Future Without Prompts
By
–
"Where we're going, we're not going to need prompts." – Emad on the future of Stable Diffusion (v3 coming in a few months! )
-
Chain-of-Thought Prompting Demo on Poe Chatbots
By
–
Another demo — chain-of-thought prompting on Poe (“Dragonfly” — there’s multiple chatbots) using the “format trick”: https://
poe.quora.com/s/Tycgjooa3Aio
SfOfLZsN
… -
Prompt Engineering Remains, But Five New More Valuable Things Will Emerge
By
–
prompt eng will always be "a thing" but this time next year we'll have found 5 more new "things" that are more valuable to build on
-
AI Model Conditioning: Prompting vs. Fine-Tuning Techniques
By
–
Generally I’d call that “conditioning the model”, which can take the form of either prompting or fine-tuning, but not sure if that’s exactly the right term. They’re two different techniques to make it respond how you want.
-
Fine-tuning for AI Specialization and Performance
By
–
Not sure I understand fully re: alignment, but fine-tuning is usually the most performant way to specialize when you have the data for it.