Evidence receipt / evaluation
Published · transcript-backedShreya Rajpal: evaluation
16 May 2023 Latent Space Guaranteed quality and structure in LLM outputs - with Shreya Rajpal of Guardrails AI
“I think the sequel thing, for example, it's very exciting because I had just released this like two days ago and then I already got some inbound with like people kinda swapping, like building these products and of swapping it out internally and you know, getting a lot of value out of what the sequel bug-free SQL provides.”
Source trail
Everything needed to verify it.
- Speaker
- Shreya Rajpal
- Attribution
- Verified speaker
- Claim type
- evaluation
- Recorded
- 16 May 2023
- Publisher
- Latent Space
Transcript context
…Right. Valid url, which we talked about. Mm-hmm. Maybe open AI is doing a little bit more of internally. Mm-hmm. Maybe open AI uses card rails. You don know be a great endorsement. Uhhuh what is surprisingly popular and what is, what do you think is like underrated? Out of all your contracts? Mm-hmm. Mm-hmm. Okay. I think that the, well, not surprisingly, but the most obvious popular ones for me that I've seen are like structure, structure type, et cetera. Anything that kind of guarantees that. So this isn't specifically in the validators, this is essentially like part of the gut, the core proposition. Yeah, the core proposition. I think that is like very popular, but that's also kind of like the first order. Problem that people are kind of solving. I think the sequel thing, for example, it's very exciting because I had just released this like two days ago and then I already got some inbound with like people kinda swapping, like building these products and of swapping it out internally and you know, getting a lot of value out of what the sequel bug-free SQL provides. So I think like the bug-free SQL is a great example because you can see like how complex these validators can really go because you end up seeing like bug-free sql. What it does is it kind of like takes a connection string or maybe a, a schema file, et cetera. It creates a sandbox SQL environment for you, like from that. And it does that at startups so that like every time you're getting like a text to SQL Query, you're not having to do pay that cost time and time again. It takes that query, it like executes that query on that sandbox in that sandbox environment and then sees if that query is executable or not. And then if there's any errors that you know, like. Packages of those errors very nicely. And if you've configured re-asking it sends it back to the model and you know, basically make sure that that like it tries to get corrected. Sequel. So I think I have an example up there in the docs to be in there, like in applications or something where you can kind of see like how it corrects like weird table names, like weird predicates, et cetera. I think there's other kind of like, You can build pretty complex systems with this. So other things in there are like it takes information about your database and then injects it into the prompt with like, here's the schema of this table. It automatically, like given a national language query, it finds like what the most similar examples are from the history of like, serving this model and like injects those into the prompt, et cetera. So you end up getting like this very kind of well thought out validator and this very well thought out contract that is, is just way, way, way better than just asking in plain English, the large language model to give you something, right? So I think that is the kind of like experience that I wanna provide. And I basically, you'll see more often the package, my immediate response is like, that's cool. It does more than I thought it was gonna do, which is just check the SQL syntax. But you're actually checking against schema, which is. Highly, highly variable. Yeah. It's…
Stored transcript either side of the excerpt. The highlighted words are the published quote; the surrounding text is unedited source, never generated.