The Hidden Flaws in ChatGPT & Friends That Could Sink Your Project
(and How to Fix It)
This week, we have the fourth free video from our recent course "AI for Business Professionals" to give you an idea of the concepts we teach in the full course, and share some insights.
In this one, we focus on this new type of "intelligence" that LLMs have, that is different from ours.
As we will see in the video, LLMs don't think like humans. The LLM isn’t reasoning. It isn’t forming opinions.
It’s just extremely efficient and surprisingly crunches probabilities, which also leads to some of its weaknesses.
What we see as “intelligent” responses materialize from a soup of statistical relationships between tokens, stacked together in a way that feels like comprehension.
What matters to us is how this system fails and what we can do to make it more reliable.
So let’s understand these weaknesses, or limitations, a bit better…
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In the last video, we went through the basics of training large language models and, really, machine learning as a whole. If you didn’t catch every detail, don’t worry — you weren’t supposed to. The goal wasn’t to make you an AI research scientist in twenty minutes but to give you just enough context to see where everything fits together. We touched on transformers, training objectives, and how LLMs don’t learn like humans. That last part is key: these models have unique strengths and weaknesses that make them powerful building blocks in some areas and unreliable in others.
These weaknesses are crucial to understand so you can know where you can build with and use LLM tools safely and appropriately in your workflows and what techniques you can use to address these issues. LLMs are not plug-and-play geniuses; they often need extra work to be practical in real-world applications.
Now, let’s take a closer look at what these models actually “learn,” where they fail, and what we can do about it.
Watch the video (or read the article version here):
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