transynthetical-engine

Here’s an approach to return just JavaScript: https://github.com/williamcotton/transynthetical-engine The key is the addition of few-shot exemplars.

I think the two-layer approach is worthwhile if only for limiting tokens! Here’s an example of what I mean: https://github.com/williamcotton/transynthetical-engine#brow... By keeping the main discourse between the user and the LLM from containing all of the generated code and instead just using that main “thread” to orchestrate instructions to write code it allows for more back-and-forth. It’s a good technique in general! I’m still too paranoid to execute instructions via email without a very limited set of abilities!

Here’s an example of a question-and-answer augmentation written in TypeScript that uses the exact approach used by PAL, et al: https://github.com/williamcotton/transynthetical-engine

Here is an example of some prompt engineering in order to build augmentations for factual question-and-answer as well as building web applications: https://github.com/williamcotton/transynthetical-engine

Hey Simon! I've been digging your writings on LLMs lately. I've been having some decent luck with some of the approaches that I've discussed in the following articles and projects: From Prompt Alchemy to Prompt Engineering: An Introduction to Analytic Augmentation: https://github.com/williamcotton/empirical-philosophy/blob/m... Writing Web Applications with LLMs: https://www.williamcotton.com/articles/writing-web-applicati... https://github.com/williamcotton/transynthetical-engine I'd love to hear your thoughts on the matter! One of the techniques that I've found for reliably returning JSON is... ask for multiple responses and then use one of the responses that successfully parses!

Sure you can. The easiest way is to go to https://chat.openai.com/chat and paste in a Wikipedia article. There are more involved manners like this: https://github.com/williamcotton/transynthetical-engine/blob...

There are many projects in the works that are having success with writing somewhat formal English language specifications and generating working software. One of my favorite recent projects is called Parsel: Parsel: A (De-)compositional Framework for Algorithmic Reasoning with Language Models https://arxiv.org/abs/2212.10561 Here's a notebook with an introduction: https://github.com/ezelikman/parsel/blob/main/parsel.ipynb And here's a GUI interface the author has been developing: http://zelikman.me/parsel/interface.html I've been working on an augmented large language model that given these few-shot exemplars can build the below fully-functional ToDo App: == https://github.com/williamcotton/transynthetical-engine/tree... https://www.williamcotton.com/articles/junie-browser-builder... All of this is still very rough around the edges, prone

Wow this is the best content I have seen on prompt engineering, it feels much more like computer science than the usual prompt engineering recipes. This formalism of analytic augmentation and higher order augmentations seems like it is ripe for some kind of library or dsl to build and evaluate and monitor these processes. But somehow I feel like these capabilities will become so standard that they will be incorporated into the official apis. [edit] Just noticed it looks like you are working on just that. I will keep an eye on this! https://github.com/williamcotton/transynthetical-engine