You've probably heard someone say, AI's gonna take your job, and someone else say, AI can't even count fingers in a photo. Both of those reactions come from, well, the same problem. We're using the word AI like it's one single magic machine. And guess what? It's not. Today I'm gonna give you a simple mental model for what AI is, what chat GPT and friends are, and how to get the benefits without falling for all the hype. Welcome back to the Tyler Woodward Project. I'm Tyler, a broadcast engineer by trade, a Linux nerd by choice, and I enjoyed demystifying tech that's supposedly quote unquote too complicated for people. Today we're answering a deceptively basic question. What is AI and what are ChatGPT, Claude, and Gemini? Here's the plan. First, we'll define AI in a way that matches how it shows up in your life. Then I'll pull the covers off of these chat tools, what they're good at, and where they get weird. Finally, I'll give you a practical checklist for using them safely and effectively. One quick note, I'm staying platform neutral. The brand names change fast, but the underlying ideas are pretty stable. So let's get into it. When people say AI, they might mean three different things. If you don't separate them, everything sounds contradictory to each other. First, AI is kind of being used as this umbrella term. This includes lots of techniques that help computers do things we associate with intelligence, you know, recognizing speech, finding patterns, making predictions, generating text, and so on and so forth. Second is machine learning. That is when you train a system on examples instead of hand coding every single rule. Spam filters, photo tagging, and recommendation systems are all common examples of this. Number three, and this is what's dominating headlines right now. Generative AI. That's when a system generates new content like text, images, audio, or code based on patterns it learned during it's it's training. Now, tools like ChatGPT, Claude, Gemini, Copilot, they're all chat style assistants built on something called an uh an LLM. It stands for a large language model. An LLM is trained on a huge amount of text, so it can predict what comes next in a sequence. A useful way to think about it is it's like autocomplete, but scaled up massively, and then tuned to follow instructions and hold a conversation, basically. And it's important to say this out loud. These tools don't know things the way a person knows things. They don't have senses, they don't have lived experience, they don't automatically check reality before they answer. What they are are great at producing plausible language. Often correct, sometimes wrong, occasionally wrong in a way that still sounds overly confident. You'll hear a term, hallucination. In this context, it means the model generated an answer that sounds legitimate, but it's not grounded in real facts. It's it's not lying the way we would think of lying in a in a human sense. It's generating the most likely next words. Here's why this matters. If you treat a chatbot like a search engine, you'll you're gonna get burned. Search engines try to point you to sources. Chatbots try to produce a response. The difference changes how you should trust the output. One level deeper, LLMs operate on tokens, which are chunks of text, think pieces of words. When you ask a question, the model predicts the next token, then the next, then the next, until it builds an answer. If you're thinking, so it's guessing. Yeah, yeah, sort of, but it's guessing with statistical models that you know it learned a ton about how language usually works. This reminds me of audio noise reduction and broadcasting. When it's dialed in, it's magic. But if you push too hard, you get watery artifacts that still sound smooth. And well, AI techs can be similar. Polished output can hide problems. So you need a quick reality check. So where do ChatGPT, Claude, and Gemini and all these other chatbots fit in? Broadly, they're different products built on different model families with different tuning and safety rules, and sometimes different add-ons like reading files, analyzing images, or using web tools. If you've ever tried two assistants with the same prompt and gotten different answers, that's normal. Different training, different tuning, different guardrails. The big beginner takeaway: don't ask which one is the smartest. Ask which one fits my task and my risk level. Let's connect this to real life with a scenario where these tools shine and fail at the same time. You've got a pile of information and you want clarity fast. Maybe you're writing a cover letter. Maybe you're trying to understand a medical bill. Maybe you're comparing laptops, or maybe you're, I don't know, staring at a long email thread and you just need actionable items. Chatbots are great at turning a messy prompt into structure. If you paste a job description in your resume and you say, draft a cover letter with a confident, friendly tone. Well, you'll usually get something usable in a few seconds. And that matters because it lowers the activation energy, if you will. You're not stuck staring at a blank page. You can react to a draft instead of inventing one from nothing. But here's the failure mode: the chatbot will happily fill in gaps with confident nonsense. If you say, compare these two laptops, but you don't provide exact model numbers and specifications, the assistant may quietly invent some details for you. Or it might assume you mean a popular model and give you a comparison that sounds right, but doesn't match what you're actually looking for. So here's a safety, uh, a safety pattern. Make the AI stay grounded in what you provide and make it show its work in a way that you can verify. Instead of asking which laptop is better, which is kind of vague, ask, here are the specs I'm looking at. Create a table of differences using only what I pasted. Then tell me which one fits video editing better and why. Now you're forcing it to stay inside the fence and you're getting a decision based on constraints that you actually care about. Another practical example, summarize a long document. These tools are excellent at pulling out themes, turning paragraphs into bullet points, and translating dense language into plain, usable English. And I'll make this personal for a second. For me, with ADHD, attention is a limited resource, all right? If I'm staring at a long article, a really overly dense PDF, or even a messy email thread, my brain can bounce off. It's just, it's not going to tune in. But if I can paste it into a chat box and say, summarize this into a detailed list, break it into sections, and pull out actionable items and deadlines, that for me has been genuinely amazing. It's been, I dare say, life-altering. It's really helped. It turns something I'd avoid normally into something I can actually start with. And starting, that's usually the hardest part, right? Now, all this goes without, you know, I I've I I've got to at least give you some caution, all right? So here's here's where we're gonna get into the caution part. If the document was legal, financial, or you know, medical implications, treat the summary like helpful notes, not not a final product. Verify important points in the original text. And if you're making a serious decision, use a qualified professional. One more cultural thing that's worth uh saying out loud. We're trained to trust confident language. These tools can generate confident language on demand. So we have to shift trust away from tone and toward verification. Let's turn this into well, a quick beginner checklist you can use today. First, tell it what you want, who it should be, and what you know, quote unquote good looks like. Act like a tutor. Explain this at a beginner level. Give one example, then quiz me. Second, add constraints so it can't drift off. Use only the information I provide. If you're unsure, ask me clarified questions before answering. I do that sometimes, and it it will indeed actually ask you stuff back to try to figure out what you're getting at, where the end goal is. Third, use it for drafts and structures, not final authority. It's fantastic for outlines, rewrites, and brainstorming. It's risky as a pure fact source, unless it can point you to verifiable references you can check. Fourth, learn the red flags. If it gives you very specific numbers, quotes, or according to a study, but doesn't give you the source of what that study actually is, treat that as a cue to verify. If it can't provide a grounded answer, you can ask it what it would need to know or what sources you should consult. And fifth, protect sensitive information. Assume anything you paste could be stored depending on the service and settings. Don't paste passwords, private keys, or confidential work documents unless you explicitly know your workflow is approved and safe. Don't get yourself in trouble. That's the point. Used well, these tools can save time and reduce friction. Used carelessly, they can quietly inject errors into important decisions. So the next time someone says AI, you can ask, do you mean machine learning in general? Or do you mean a generative chatbot, an LLM that's great at language, but not automatically great at truth? Visit TylerWodward.me, follow at Tylerwoodward.me on Instagram and threads, subscribe and like the show on your favorite podcast platform. I'll catch you next week.