Five surprising truths about how AI Chatbots work today 2026

David Hansen

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AI chatbots are now firmly established in our lives but how many really know how they work? Some of the most surprising information about AI chatbots can help us understand how they work, what they can and cannot do, and how to use them in a better way. Here are five useful facts about them.

εικόνες AI chatbots κίνδυνοι

They are trained through human feedback AI chatbots are trained in multiple stages, starting with something called pre-training, where models are trained to predict the next word in huge text datasets. To make them useful and safe for discussion, human "commentators" help guide models toward safer and more useful answers, a process called alignment. Without it AI chatbots would be unpredictable, potentially spreading misinformation or harmful content. OpenAI, the company that developed ChatGPT, has not disclosed how many employees have trained ChatGPT for how many hours. They don't learn with words – but with the help of tokens Humans naturally learn language through words, while AI chatbots rely on smaller units called tokens. These units can be words, subwords, or obscure strings of characters. While tokenization generally follows logical patterns, it can sometimes give rise to unexpected idiosyncrasies of language-interpreting AI chatbots. The vocabulary of modern AI chatbots typically consists of 50,000 to 100,000 tokens. Their knowledge is outdated with each passing day AI chatbots are not constantly updated and therefore may struggle with recent events, new jargon, or generally anything beyond their knowledge limit. A knowledge threshold refers to the last time an AI chatbot's training data was updated, so they are not aware of events, trends, or discoveries after that date. The current version of ChatGPT has an end date of June 2024. Bing results are filtered based on relevance and credibility of the source. They make false claims very easily AI chatbots sometimes make false or silly claims with confidence because they predict text based on patterns instead of verifying facts. These errors stem from the way they work: they optimize consistency instead of accuracy, rely on imperfect training data, and don't understand the real world. They use calculators to do mathematical calculations A recently popular feature of AI chatbots is called reasoning. Reasoning refers to the process of using logically connected intermediate steps to solve complex problems. This is also known as "chain thinking" reasoning. Instead of jumping directly to an answer, thought chaining allows AI chatbots to think step by step. This hybrid approach helps improve reliability in complex tasks. SOURCE

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Author

David Hansen

An engineer, author and high-tech enthusiast whose passion has been evolving for 12 years. After contributing to numerous online publications, David shares his experiences and discoveries on Grand Goldman. He focuses on Smart Homes, Connected Devices, and how they will improve our lives and society in the future.

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