Ep 867: 2026 LLM Cheat Code: 10 Essential Steps To Get the Most out of Any AI Chatbot (Start Here Series Vol 26)
episode
Everyday AI Podcast – An AI and ChatGPT Podcast
40 min
2 speakers
8 chapters
transcribed 16 hours ago
Transcript
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Transcript generated automatically by AI and may contain errors.
Why is keeping up with AI changes compared to drinking from a fire hose?
Welcome to the Everyday AI podcast. My name is Jordan Wilson, and for the past three and a half years, we put out more than 800 episodes. Yet, one of the most common questions I get, I didn't really have an answer for. Where do I start on the Everyday AI podcast? And that's why we started the Start Here series. And with Fall now back in full swing, the Everyday AI podcast is Is going back to school and playing back the entire Start Here series from front to back. We've hit pause on our normal Monday to Friday programming to run back our most popular series ever for the next 30 days. We made the Start Here series for beginners and AI champions alike. So whether you're just trying to get a grasp on large language models or grappling with the best coding harness for multi-agentic.
Workflows, the Start Here series covers it all. Plain language, no jargon, and easy to follow along each day. So make sure to subscribe to the podcast and check back each day for new insights day by day. The series is a culmination of spending more than 10,000 hours covering generative AI over the past three and a half years. So you don't want to miss a single episode of the Start Here series. Let's get into it. The saying of keeping up with AI is like trying to drink water from a fire hose, though it's tired and cliche is obviously a hundred percent true. I mean with all the nonstop updates, your head is probably spinning trying to keep up. So you're thinking with all these new releases, how do I use ChatGPT?
Or is using Claude kind of the same as using Gemini on the desktop? Or can I prompt Copilot kind of like I'd prompt Codex? Or maybe you've had to start the race sprinting and you never really got the proper 101 on how all of these AI chatbots work. Regardless, you definitely aren't alone in the fire hose updates drowning you out. That's the new struggle for every enterprise, because it's not like there's a cheat code, right? Because as the AI models are changing almost daily, so too does the input required to get the best output. And I get it. As someone that covers AI daily, I understand the struggle of trying to play a game, yet the field dimensions change without notice. And you were using a softball yesterday, but cricket gear today and tomorrow, you might kind of play it out like rugby.
The input rules and the output capabilities are moving targets. But there's one thing I've found out over the last three or four months. The big players have all just kind of started to copy each other, which is actually a good thing for you. So I think it's actually. been over the last two months that we've finally stumbled on a set of best practices for getting the best outputs out of any large language model. So that means, well, there is a cheat code, or at least a set of somewhat concrete rules that when followed, will give you stellar outputs really no matter what model you're using. And that's exactly what we're going to be giving you today on everyday AI. Welcome to the Start Here series. Uh, if you're new here, the Start Here series is your essential.
Guide to getting caught up and getting ahead with AI. But first, let's talk about the big picture. So The capabilities of these models are very, very real. And it is hard to keep up. Right. So Right now. You probably see examples all over the place. You see people sharing examples, whether it's online, whether it's media articles that are so poorly written, and you're like, well, these AI models are really bad. Look at all these mistakes. But then you also see these benchmarks and these tests and you know, these big companies laying people off in lieu of spending billions of dollars on AI, and you're confused because you can't even know or don't even really understand what button to click or Or which model should I use?
And up until recently, it's been too hard to follow because there's been too many different paths. And now I think essentially we have cookie cutters, right? We have the McMansions of models because They're all kind of the same. Obviously the capabilities and the harnessing of the tool is all completely different and unique, but
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Chapters
8 chapters
1
Why is keeping up with AI changes compared to drinking from a fire hose?
0:00–5:30
2
What is the “2026 LLM Cheat Code” and why does it matter now?
5:30–10:01
3
How do the big AI players (ChatGPT, Claude, Gemini, Copilot) converge into cookie‑cutter models?
10:01–14:42
4
What are the 10 essential steps to get the most out of any AI chatbot?
14:42–20:02
5
Why should you choose a single AI operating system and stick with it?
20:02–24:11
6
How does the context layer and context window affect AI output quality?
24:11–28:33
7
What are the best practices for integrating files, apps, and company data with AI?
28:33–34:10
8
How do transparency, observability, and verification loops ensure reliable AI workflows?
34:10–40:50
Speakers
2 identifiedMore from Everyday AI Podcast – An AI and ChatGPT Podcast
Ep 866: Build, Buy, Partner, or Wait: The 4-Layer AI Stack Decision Framework for 2026 (Start Here Series, Vol 25)
Ep 865: Open Source AI 101: Why Local Models, Cheap APIs, and AI Agents Change Everything (Start Here Series Vol 24)
Ep 864: Headless Software: Why Companies Are Building Software for AI Agents, Not Humans and what it means (Start Here Series Vol 23)
Ep 863: Agentic Context Carry: 3 Steps to Improve Cowork and scheduled AI Workflows (Start Here Series Vol 22)
Ep 862: AI Change Management That Works: 5 Moves The Top 5% Make (Start Here Series Vol 21)
Ep 861: The 7 Silent Sins of Doing AI Right: How to Spot and Overcome the Invisible AI Work Traps (Start Here Series Vol 20)