NeMo-Guardrails VS transynthetical-engine

- https://github.com/NVIDIA/NeMo-Guardrails/

Here's "jailbreak detection", in the NeMo-Guardrails project from Nvidia:

https://github.com/NVIDIA/NeMo-Guardrails/blob/327da8a42d5f8...

I.e. they ask the llm if the prompt will break the llm. (I believe that more data /some evaluation on how well this performs is intended to be released. Probably fair to call this stuff "not battle tested".)

I have been playing around with some models locally and creating a discord bot as a fun side project, and I wanted to setup some guardrails on inputs / outputs of the bot to make sure that it isn't violating any ethical boundaries. I was going to use Nvidia's Nemo guardrails, but they only support openai currently. Are there any other good ways to control inputs?

Thanks, I hadn't seen those. I did find https://github.com/NVIDIA/NeMo-Guardrails earlier but haven't looked into it yet.

I'm not sure it solves the problem of restricting the information it uses though. For example, as a proof of concept for a customer, I tried providing information from a vector database as context, but GPT would still answer questions that were not provided in that context. It would base its answers on information that was already crawled from the customer website and in the model. That is concerning because the website might get updated but you can't update the model yourself (among other reasons).

you can use guard rails with langchain - https://github.com/NVIDIA/NeMo-Guardrails

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 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...

https://www.williamcotton.com/articles/writing-web-applicati...

https://github.com/williamcotton/transynthetical-engine

I'd love to hear your thoughts on the matter!

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...

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 to errors of various kinds, and generally not ready for prime time, but anyone is welcome to play around with what is there!