kor VS apollo-elements

Check out kor

Extract structured data from text using LLMs (https://github.com/eyurtsev/kor)

Check out https://github.com/eyurtsev/kor

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

Currently, I'm experimenting with GPT-3.5-turbo in conjunction with the kor library (langchain for information extraction) to define a prompt template with various examples of what I'm looking for.

yes. there are a few approaches which i intend to take and some helpful resources:

You could implement a Dual LLM Pattern Model https://simonwillison.net/2023/Apr/25/dual-llm-pattern/

You could also leverage a concept like Kor which is a kind of pydantic for LLMs: https://github.com/eyurtsev/kor

in short and as mentioned in the README.md this is absolutely vulnerable to prompt injection. I think this is not a fully solved issue but some interesting community research has been done to help address these things in production

Using vanilla JS or TS or using web components and want to have a framework-independent way of doing things? You can stick to the GraphQL codegen itself since it takes care of almost everything underneath. Or if you want, you can also use Apollo Client’s vanilla version @apollo/client/core. Apollo Elements does come with support for a lot of webcomponent libraries like Lit, Fast and Gluon or even without any of it and hence is quite flexible.