Controlled and compliant AI applications

Output Structuring

The challenge of structuring output from large language models (LLMs) is crucial for ensuring consistency and reliability in business applications. Daniel Whitenack explains that while prompt engineering can help, it doesn't fully solve the problem of varied outputs from LLMs. He emphasizes the need for systems like Prediction Guard to constrain and control output types, such as sentiment tags or valid JSON blobs, to facilitate automated business decisions 1. Chris Benson notes that integrating these models into existing workflows requires minimal expertise, making it accessible for developers and data scientists 2.

   

Validation Methods

Validation methods, including factuality and toxicity checks, are essential for making AI outputs safer and more reliable. Daniel Whitenack describes how Prediction Guard implements factuality checking scores and consistency checks to address hallucination problems in LLM outputs 3. He also highlights the importance of ensembling multiple models to ensure reliable outputs. Chris Benson acknowledges the robust pipeline Prediction Guard offers, which includes structured output and additional checks for business applications 4.