Reducing hallucinations in LLM agents with a verified semantic cache using Amazon Bedrock Knowledge Bases

This article discusses a novel approach to reducing hallucinations in Large Language Models (LLMs) using a verified semantic cache with Amazon Bedrock Knowledge Bases and Agents.

• Introduces a solution that checks user queries against a curated, verified knowledge base before generating LLM responses
• Uses semantic similarity scoring to determine response strategies:
  • Strong match (>80%): Return verified answer directly
  • Partial match (60-80%): Use cached answer to guide LLM response
  • Low match (<60%): Use standard LLM processing
• Key benefits include:
  • Reduced costs by minimizing unnecessary LLM invocations
  • Improved response accuracy
  • Lower latency for known queries
• Requires careful curation of verified question-answer pairs and ongoing cache management

The solution provides a practical approach to improving LLM reliability by leveraging a semantic cache with trusted, verified information.