Exploring Trust But Verify: A New Paradigm for Gen Native Application Development

As AI applications continue to transform industries, the importance of trust and correctness in machine learning models cannot be overstated - introducing Guardrails AI, a novel programming paradigm that ensures reliable outputs by verifying every model response.

1. Shrea Rajal, co-founder and CEO of Godreal AI, will discuss the new programming paradigm "trust but verify" for generative AI applications.
2. Shrea has a background in machine learning and previously worked at Prase, an infrastructure machine learning company.
3. She also spent time in the self-driving car industry and did research in classical AI and deep learning.
4. There is currently a lot of excitement about generative AI, but there are also concerns about its reliability.
5. The idea behind "trust but verify" is to build systems that assume the AI will make mistakes, and include checks to catch and correct those mistakes.
6. This paradigm is in contrast to traditional programming, where the goal is to create a system that is error-free from the start.
7. Generative AI models, such as language models, are prone to making errors because they are trained on large amounts of data and may produce outputs that are not factually correct or relevant to the
8. The "trust but verify" approach involves adding checks to the output of generative AI models to ensure that they are accurate and relevant.
9. These checks can include things like validating the output against a database or using external systems to verify its accuracy.
10. One way to implement these checks is to ground the output in an external system, such as hooking up the output of a language model to a runtime that contains application-specific data.
11. Another approach is to use rule-based heuristics or traditional machine learning methods to solve basic constraints.
12. The "trust but verify" paradigm can be applied to any type of generative AI, including text, images, and audio.
13. It is important to note that this paradigm does not eliminate the need for careful prompt engineering and fine-tuning of models.
14. However, it adds an additional layer of protection against errors and increases the reliability of generative AI systems.
15. The "trust but verify" approach can help build trust in generative AI and increase its adoption in a wider range of applications.

Source: AI Engineer via YouTube

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