LLM Weaknesses 101: What They Really Learn
LLMs Don’t Think—They Predict. Here’s Why That Matters
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. Learn more in today's video (or article lesson here):
🚀 Ready to actually use AI at work—without needing to code?
If you’re a business professional who wants to lead with AI instead of watching from the sidelines, this course is for you. You’ll learn how to:
Use tools like ChatGPT, Claude, Gemini, and Perplexity effectively
Master prompting, reasoning, and no-code workflows
Automate research, writing, analysis, and team tasks
Create custom AI solutions that save hours every week
Drive smart, strategic AI adoption in your team or company
👉 Join the July cohort for our kick-off call of the “AI for Business Professionals” course here: https://academy.towardsai.net/courses/ai-business-professionals?ref=1f9b29
No technical background needed. Just real-world AI skills designed to boost your output, leadership, and career.