LLMs are like Humans - They make mistakes. Here is how we limit them with Guardrails

Taming the Probabilistic Engine: Why we can't "fix" AI Hallucinations, but we can Shield them.

Introduction

It happened again today. While discussing the AWS BeSA program, my LLM started daydreaming. It insisted there was a local IT event in my city that simply doesn’t exist. It was so convincing that I had to correct it three times.

As Marko Sluga recently put it in a chat: LLMs are probabilistic engines that prioritize coherence over facts. Just like (his words) "Karen from accounting," they sometimes try to justify a point even when they lack the data. That core nature will never go away.

However, while we can't prevent hallucinations 100%, we can highly limit their impact.

In this post, I’ll dive into how Grounding and Amazon Bedrock Guardrails act as the essential quality control layer to keep AI outputs within professional boundaries.
It took multiple corrections to get the model back on track. While funny in a private chat, this is a massive risk for businesses. Here is how we solve this using Amazon Bedrock.

The Logic of Hallucinations LLMs are "next-token predictors." They don't have a concept of "truth"; they have a concept of "probability." If a model is tuned to be highly creative, it will often prefer an invented answer over no answer at all.

Step 1:
Grounding through RAG To fix this, we implement Retrieval-Augmented Generation (RAG). We provide the model with a specific set of documents. The model is then instructed to answer only based on that provided context.

Step 2:
Implementing Amazon Bedrock Guardrails Based on my discussion with Marko Sluga today.

I learned that "Guardrails" act as the ultimate "quality control" layer.

They offer:

Contextual Grounding Checks: The system runs a real-time analysis. If the "Relevancy Score" (how much the answer matches the source data) falls below a certain threshold, the output is blocked.

Defined Fallbacks: Instead of letting the model "wander off," you configure a standard response: "I cannot answer this based on the available data."

Safety & Compliance: Guardrails also handle PII (Personally Identifiable Information) redaction and toxic content filtering, ensuring the AI stays within professional boundaries.

Conclusion The difference between a "chatbot" and an "Enterprise AI Agent" is Control. By using Amazon Bedrock Guardrails, we move from a probabilistic guessing game to a reliable system that prioritizes accuracy over creativity.