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When I first started exploring AI, it felt like learning a new language.
In fact in last Newsletter where I explored an ultimate prompt for ChatGPT 4 & 5. I got replied that We still get confused all the time with AI Jargons!!! 🙃
Same here, Everyone around me was talking about LLMs, NLP, inference, fine-tuning, and reinforcement learning like they were discussing their weekend plans.
Meanwhile, I was nodding politely… while my brain was buffering.
I could use ChatGPT and test new AI tools, but I realized—without understanding the actual terms,
I was like a tourist who could order coffee in a foreign country but couldn’t ask where the bathroom was.
This frustration made me actively seek resources that break down AI jargon into plain English.
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The Resource That Changed the Game
Recently, I came across a brilliant LinkedIn post by Adam Biddlecombe that explained 40 AI terms in the simplest way possible.
The image he shared is basically a cheat sheet for anyone wanting to “speak AI” without feeling overwhelmed.
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Here’s a brief digest of a few terms from Adam’s post:
Bias: When AI gives unfair results because the data it learned from was skewed.
Model: The final program AI creates after learning from data.
Dataset: The big pile of information AI learns from.
Token: Tiny chunks of text (words or pieces of words) that AI uses to read and write.
Overfitting: When AI gets too good at training examples but fails on new situations.
Generative AI: AI that can create new stuff like images, music, or text.
Transfer Learning: Using what an AI learned in one task to speed up learning in a new task.
Reinforcement Learning: Teaching AI through trial and error with rewards and penalties.
LLM (Large Language Model): An AI that understands and generates human-like language.
NLP (Natural Language Processing): How AI understands and works with human language.
What I loved most about Adam’s visual is that it strips away the complexity and keeps only the “aha!” factor.
The moment I read through it, I could finally connect the dots:
So that’s why people keep saying “fine-tuning” in AI tool updates.
Now I understand what “tokens” mean when ChatGPT says I’ve hit my limit.
Ah… reinforcement learning is basically how AI learns through trial and error.
The best part?
I didn’t just read the definitions.
I started using them in my own conversations about AI, and suddenly those discussions felt way less intimidating.
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Learn AI or Get Left Behind
You must save such useful posts and keep getting back to it to absorb this like you absorbed all the alphabets in your nursery class. 😀
AI isn’t just a trend, it’s becoming the default operating system for how we work, create, and communicate.
If we don’t learn the language of AI, we risk being outsiders in a world that’s moving faster than ever.
In the coming weeks, I’m going to break down more AI concepts, tools, and strategies just like this, so you can not only use AI but also understand it.
Stay tuned… the AI glossary is just the beginning.
If you missed Tuesday’s issue read it here → ChatGPT Ultimate Problem Solving Prompt
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