Just using the term “LLM” in a conversation with your friends and colleagues can bring about a sense of pride, marking the accomplishment of being able to repeat some AI jargon. What happens when you’ve sparked some interest with someone in your group and they want to take it a whole lot further. Here is your plain english pocket guide to AI jargon that will not only keep you sounding up to speed on the topic it will also give you some questions to spark some curiosity in others.
Imagine walking into the biggest library in the world. You ask the librarian a question, and instead of pointing you to a book, she instantly tells you the answer in a full conversation. That’s what an LLM does—it reads tons of information and responds like a super-smart librarian who never sleeps.
Conversation Starter: How do you think LLMs will change the way we work?
Picture a little kid learning to ride a bike. At first, they wobble and fall, but after enough tries, they get better without needing help. That’s how machine learning works! AI looks at lots of examples, learns from mistakes, and improves over time—just like a kid mastering their bike.
Conversation Starter: Have you noticed any AI-powered recommendations getting better over time?
Imagine a detective trying to solve a mystery. Instead of working alone, they have a team, where each person looks at a small clue. When they put all their clues together, they solve the case! Neural networks work the same way—each layer of "detectives" processes information to figure out an answer.
Conversation Starter: Do you think AI can truly ‘think’ like a human?
Imagine you have a magic spellbook, but the spells only work if you say them just right. If you say "Make me a sandwich," nothing happens. But if you say "Summon a delicious turkey sandwich with extra cheese," poof! The perfect sandwich appears. That’s what prompt engineering is—finding the right words to get the best response from AI.
Conversation Starter: What’s the most interesting prompt you’ve seen someone use?
Have you ever heard a little kid tell a story that sounds amazing but isn't actually true? Like, "I saw a dragon on my way to school!" AI sometimes does the same thing—it confidently makes things up! This is called a "hallucination" in AI, and it happens when AI tries to guess an answer but gets it wrong.
Conversation Starter: Should AI be more cautious in giving confident but wrong answers?
Imagine a chef who can cook anything but decides to become a master at making sushi. They practice over and over until they make the best sushi in town. AI models can do the same thing! When we fine-tune them, we train them to be experts in specific areas, like law, medicine, or customer support.
Conversation Starter: What industry do you think could benefit most from fine-tuned AI models?
Right now, robots and AI are like toys that can only do one thing—one plays music, another tells jokes, another answers questions. But AGI is like a magical toy that can do anything a human can do—think, learn, and even make decisions. It’s still science fiction, but some people believe it could happen one day.
Conversation Starter: Will AGI ever happen, or is it just science fiction?