I found guardrails AI really useful for my research on LLMs. Otherwise I would have wasted a lot of time trying to curate the outputs from my experiments.<p>Don’t cram too much in a single prompt though. Prompt structures like guard rails naturally carry high cognitive load for the LLM, which leads to biased outputs. I found the best practice is to alleviate it by using multiple prompts and using a guard rail as an end-step rather than one big prompt for the LLM. (<a href="https://arxiv.org/abs/2402.01740" rel="nofollow">https://arxiv.org/abs/2402.01740</a>)
Pretty interesting to go from a world of deterministic code, to LLMs which can do incredible things, but unreliably. In a world of LLMs, I could imagine guardrails being a table-stakes part of engineering an ML system, just like unit tests, and CI/CD would be for traditional software.
Hi everyone, the CEO of Guardrails AI here! We're stoked to launch Guardrails Hub as an open source framework to solve AI reliability.<p>Check out the hub here -> <a href="https://hub.guardrailsai.com/" rel="nofollow">https://hub.guardrailsai.com/</a>