In terms of applications with it, I have made things like sketch: https://github.com/approximatelabs/sketch https://github.com/approximatelabs/sketch Raw promp

In terms of applications with it, I have made things like sketch: https://github.com/approximatelabs/sketch https://github.com/approximatelabs/sketch Raw prompt-structure ideas i've worked with: - Iterate on a prompt with another "discriminator" prompt, that determines the result is good / safe - Write N-trials of an answer, then use another prompt to select the best answer - When doing code writing (SQL or Pandas) write the output, then use a parser (eg. `ast` in python) to validate code is valid, if not, feed back into a prompt for fixes - Logical negation checks (check if X, and if ~X, give opposite answers, then it's likely consistent, if it's both "affirmative" (as the models tend to bias towards), then it's definitely hallucinating) Other 'product' ideas i've tried: - A chat style interface (I made a chat-bot last year, similar to chatGPT) - A "google-this-for-me" style chain, that checks google, summarizes multiple results, then synthesizes a final result Ideas I've been sitting on, that I think would be fun to prototype: - An iterative "large document" editor: storing global intent, instructions, outline, and the raw text, and each iteration of the prompt works on the these objects to build a large document. - A "research this topic for me", similar to the above, but include the google searching, summarizing, and such - A code-repository "AI agent" that takes `Issues` and `Pull requests` as input, and writes and edits code for you, and by adding feedback in github, it uses that to modify the branch and act as a developer. (Code via github interface, rather than an IDE)