Evals and Guardrails in Enterprise workflows (Part 3) — Adaptive Safety Net

AI Impact Summary

This document outlines a pattern for dynamically correcting model behavior in enterprise applications, particularly within RAG systems, to mitigate risks associated with hallucinations and misinterpretations of external data. The ‘Behavior Shaping’ loop – scoring, feedback, and correction – allows models to learn from errors in real-time, adapting their responses based on evaluation results. This is crucial for applications like autonomous customer service chatbots and trading bots that rely on external data and can be vulnerable to misinterpreting volatile or complex information, preventing potentially dangerous outcomes.