This is a short summary of Data Driven NYC’s interview to the co-founder and CEO of Guardrails AI Shreya Rajpal.
Guardrails AI offers:
Guardrails AI is a fully open source library that assures interactions with Large Language Models (LLMs). It offers
- Framework for creating custom validators.
- Orchestration of prompting → verification → re-prompting.
- Library of commonly used validators for multiple use cases.
- Specification language for communicating requirements to LLM.
The company is being build right now:
- Run time guards.
- Validators: Independent check you do for any identified risk that you have registered (for instance: a given example of hallucination).
A Wardley Map to visually illustrate the problem Guardrails AI is trying to solve:
- Independent validation and verification is something that will be happening in the near future, not only for compliance reasons but for real time check that enable the Gen-AI solution to do not fall into mistakes or erosion of the brand.
- Guardrails AI is being build right now, so by that reason is in red in the map.
Retrieval-Augmented Generation (RAG)
It’s a technique that combines the abilities of a pre-trained language model with an external
knowledge source to enhance its performance, especially in providing up-to-date or very specific information.
A simple explanation:
- Retrieval: When you ask a question, the system first retrieves relevant information from a large database of text. It’s like looking up reference material to find the best possible answers.
- Augmented: The information retrieved is then combined with the knowledge already present in the language model. This enhances the model’s ability to generate a response.
- Generation: Finally, the system generates a response using both its pre-trained knowledge and the additional information it just retrieved.
The advantage of RAG is that it allows the model to provide more accurate and up-to-date responses than it could with just its pre-trained knowledge.
As usual, any constructive feedback is welcome.