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

Source: Dev.to

Introduction

It happened again today. While discussing the AWS BeSA program, my LLM started day‑dreaming. 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 (in 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.

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 an essential quality‑control layer to keep AI outputs within professional boundaries.

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 mitigate this, we implement Retrieval‑Augmented Generation (RAG). We provide the model with a specific set of documents and instruct it to answer only based on that provided context.

Step 2: Implementing Amazon Bedrock Guardrails

Based on my discussion with Marko Sluga, “Guardrails” act as the ultimate quality‑control layer. They offer:

• Contextual Grounding Checks – Real‑time analysis of the answer’s relevance. If the Relevancy Score (how well the answer matches the source data) falls below a threshold, the output is blocked.
• Defined Fallbacks – Instead of letting the model “wander off,” you configure a standard response such as “I cannot answer this based on the available data.”
• Safety & Compliance – Guardrails also handle PII 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.