guardrails VS guidance

You're absolutely correct, and I agree that there's potentially a risk of quality loss. But likewise, since these are all intrinsically linked, it may be possible to leverage strength by combining these tasks. I'm unaware of a paper reviewing the reliability and/or performance of LLMs in this specific scenario. If you find any, do share :) With regards to generating JSON responses - there are simple ways to nudge the model and even validate it, using libraries such as https://github.com/promptslab/Promptify, https://github.com/eyurtsev/kor and https://github.com/ShreyaR/guardrails

There are a couple different approaches:

- Use multi-shot prompting with something like guardrails to try prompting a commercial model until it works. [1]

- Use a local model with something with a final layer that steers token selection towards syntactically valid tokens [2]

[1] https://github.com/ShreyaR/guardrails

[2] "Structural Alignment: Modifying Transformers (like GPT) to Follow a JSON Schema" @ https://github.com/newhouseb/clownfish.

👍 validate and correct the outputs of LLMs using guardrails

not vouching for it, but I know this is at least a thing that exists and I like the general idea: https://github.com/shreyar/guardrails

This IS Microsoft Guidance, they seem to have spun off a separate GitHub organization for it.

https://github.com/microsoft/guidance redirects to https://github.com/guidance-ai/guidance now.

... and it sets the value of "armor" to "leather" so that you can use that value later in your code if you wish to. Guidance is pretty powerful, but I find the grammar hard to work with. I think the idea of being able to upload a bit of code or a context-free grammar to guide the model is super smart.

https://github.com/microsoft/guidance/blob/d2c5e3cbb730e337b...

Here's one thing I don't get.

Why all the rigamarole of hoping you get a valid response, adding last-mile validators to detect invalid responses, trying to beg the model to pretty please give me the syntax I'm asking for...

...when you can guarantee a valid JSON syntax by only sampling tokens that are valid? Instead of greedily picking the highest-scoring token every time, you select the highest-scoring token that conforms to the requested format.

This is what Guidance does already, also from Microsoft: https://github.com/microsoft/guidance

But OpenAI apparently does not expose the full scores of all tokens, it only exposes the highest-scoring token. Which is so odd, because if you run models locally, using Guidance is trivial, and you can guarantee your json is correct every time. It's faster to generate, too!

Perhaps something as simple as stating it was first built around OpenAI models and later expanded to local via plugins?

I've been meaning to ask you, have you seen/used MS Guidance[0] 'language' at all? I don't know if it's the right abstraction to interface as a plugin with what you've got in llm cli but there's a lot about Guidance that seems incredibly useful to local inference [token healing and acceleration especially].

[0]https://github.com/microsoft/guidance

LangChain is just too much, personal solutions are great, until you need to compare metrics or methodologies of prompt generation. Then the onus is on these n-parties who are sharing their resources to ensure that all of them used the same templates, they were generated the same way, with the only diff being the models these prompts were run on.

So maybe a simpler library like Microsoft's Guidance (https://github.com/microsoft/guidance)? It does this really well.

I am unclear on the status of the project but here is the conversation that seem to be tracking it: https://github.com/microsoft/guidance/discussions/201

Let's have a subreddit about Guidance!

You should look at guidance for an LLM to fill out a template. Define the output data structure and provide the real estate listing in the context (see the JSON template example here https://github.com/microsoft/guidance)