More data doesn't always mean better AI: how to make a real impact with language models

It is a common misconception: thinking that more data automatically leads to better output from a language model. Of course, data is important, but just like humans can't learn the whole world's history all at once, an AI model works much more effectively to provide information in a structured, step-by-step way. In this article, I'll explain why “more” isn't always “better” and how you can effectively train a language model to add real value within your organization.

More data, less results?

When training AI systems, such as language models, the amount of data is not the only factor that determines the quality of the output. In fact, blindly adding huge data sets can actually lead to confusion, contaminated results and a loss of focus. Think of a person trying to learn everything at once — that's overwhelming and causes poor retention.

Instead, it is much more effective to divide data into bite-sized chunks and offer it in a targeted manner. This process is known as the “chain of thought” approach: a method where you guide a model step by step through the instructions and data it needs.

What is “chain of thought”?

“Chain of thought” is a technique where you not only provide data to a language model, but also provide structured reasoning and context. So the model is not simply presented with a lot of information, but is carefully guided in how to process and apply that information.

A practical example:

• Let's say you want to train a model to write texts about history.
• Instead of offering all historical epochs at once, start with the Middle Ages, for example. Then you will continue with the Roman period, and so on.
• Through this phased approach, the model not only learns facts, but also the relationship between different epochs.

Why does this work better?

Like humans, a language model works better when it receives clear instructions and structured information. This helps the model to:

1. Generate relevant output: The model knows what to do and stays focused on the task at hand.
2. Improving quality: By providing context and examples step by step, you prevent errors and unnecessary noise.
3. Staying flexible: The model can better deal with new, unexpected input if it has learned a clear “way of thinking”.

Also read: Improve your AI with good data: find out what good data is and how to use it

How do you apply this in your organization?

If you want to use AI within your company or organization, it is essential to apply these principles. Here are a few steps you can take:

1. Start with clear goals: What do you want the language model to do? Think of specific tasks such as customer service, text generation or data analysis.
2. Provide structured data: Make sure that the data you use is relevant to the purpose and well organized.
3. Work with sample instructions: Give the model examples of how it should approach certain tasks.
4. Evaluate and optimize: Test the model's output regularly and make adjustments where necessary.

Also read: Why there will be a shortage of data in 2 years and what we can do about it

Just like a person learns better through step-by-step explanations, a language model works better if you instruct it with care. It's not about how much data you put into it, but how you use and offer that data.

Do you want to know how to apply this principle in your organization? Go to our website of The Automation Group

Conclusion: More data isn't always better. It's about smart data, clear instructions and a step-by-step approach. This is how you get the most out of AI and language models.

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