3 essential tips for businesses wanting to work with AI language models

There are three important essentials you need to know if you want to start working with language models and customising them.

1. An untrained language model is a generalist, not a specialist

First of all, it's very important to realise that an untrained language model is a generalist. Language models like ChatGPT, Gemini, Copilot or Anthropic's Claude are trained on internet data. This means they primarily possess generic information and knowledge of what is generally available on the internet. It is a very good generalist and not a good specialist. So, if you want to use a language model for specific tasks, you will need to train it, and the standard version of the language model will be unsuitable.

1. it’s all about statistics

The second thing you need to know is that a language model is a statistical model, and all the rules of statistics you undoubtedly learned at university or college also apply here. We often talk about hallucinating, and we often find it very strange that a language model hallucinates. But a hallucinating language model is very logical; it's a logical statistical phenomenon. Now, what is hallucinating again? It's when such a language model essentially starts to create its own reality and seeks confirmation for falsehoods. And why does this happen? Well, statistics is always seeking confirmation, looking for patterns. It's a statistical fact that once a pattern is found, evidence is sought to confirm that pattern. And that's why it's very important to have statistical knowledge if you're going to customise a language model. Because how can you prevent hallucinations? This involves, among other things, looking at the parameters of the language model. Essentially, the statistical equation. You have, for instance, the 'temperature' parameter. And a temperature actually indicates the extent to which a language model is allowed to think freely. Should it provide very strict answers? Or is it allowed to think a bit creatively and come up with its own suggestions? And these kinds of parameters in the statistical model of the language model are among the means to prevent hallucinations. But hallucinating itself is a very logical, statistical phenomenon. That's why you should also approach it from a statistical point of view.

1. Data were, are, and remain the key

Thirdly, data is key. We talk a lot about data, and there are quite a few misconceptions when it comes to data and language models. Many people think, 'well, these language models are already trained, so why do you still need data?' Well, you need that data to make such a language model a specialist. To turn it from a generalist into a specialist. Because that data contains knowledge and information. And if you feed a language model with that specific knowledge and information, you can truly train it on your company-specific data and guidelines. And the better the data you feed to the language model, the better you can ensure that the language model provides reliable and consistent answers. This can significantly help with cognitively complex tasks in your work. And this is the third essential that is incredibly important if you want to start working with language models yourself.

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