Reducing hallucinations in large language models with custom intervention using Amazon Bedrock Agents

This article discusses a method for reducing hallucinations in large language models (LLMs) using Amazon Bedrock Agents and a custom intervention approach.

• Hallucinations occur when LLMs generate plausible but factually incorrect information
• The solution uses Retrieval Augmented Generation (RAG) to ground responses in factual data
• A custom hallucination detection mechanism uses RAGAS metrics to score response accuracy
• If the hallucination score is below a threshold, the system triggers a human-in-the-loop notification via Amazon SNS
• The workflow uses Amazon Bedrock Knowledge Bases and Agents to create a dynamic, customizable solution

The approach provides a flexible method to detect and mitigate AI-generated inaccuracies by involving human experts when the AI's confidence is low.