Guardrails AI, interview

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 Map

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.

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