AI Hallucinations are a real challenge. When AI confidently generates wrong answers, it can lead to serious problems—especially in areas like healthcare, finance, and enterprise applications.

Amazon is working on a technique called Automated Reasoning to tackle this issue.

👉 Let me explain this with a simple example?
Think of AI as a student taking an exam. Traditional AI answers questions based on what it has seen before, but it doesn’t always check if its answer is logically sound. Automated Reasoning is like a second layer of verification—it double-checks the answer using math, logic, and structured knowledge to ensure accuracy before presenting it.

👉 How does it work?
Automated reasoning uses formal logic to validate AI outputs. If the AI generates something that contradicts known facts, rules, or structured data, it flags or corrects it before responding.

Amazon isn’t the only one working on reducing AI hallucinations. All the leading companies are trying to reduce hallucinations by adopting different methods. I have captured a few of the prominent techniques that are being leveraged by the leading players in the table below for your quick reference.

BTW – Automated Reasoning is not new. It is a formal logic and theorem that has existed for decades. We are now seeing companies like Amazon integrating it at scale with AI models, which wasn’t practical before due to computing limitations.

I have strongly believed that the future will not be binary where we will have to choose only one option. Based on the use cases and customer requirements, we will leverage a combination of these techniques to build the guardrails that will prevent the models from hallucinating. Remember, adoption of AI in use cases depends primarily on their reliability, accuracy, and security.

This is critical as AI moves into high-stakes areas where mistakes can’t be ignored.