I gave AI the reigns to book and trip.. But before m it booked a single thing, my AI assistant checked if Egypt was even safe. Then it told me what needed to be booked first. Not the flight, not the hotels... a boat on the Nile that sells out fast. I’ll drop whole story in the comments.👇 Save this for your next big trip. I sure hope this works out!

I'm gonna book a flight, entirely with the use of AI. I'll just be overseeing everything, and I'm gonna let AI take the reigns. I'll keep you updated on the journey.

I want to learn how to set up my AI agent in ChatGPT and Claude. Any help or direction would be greatly appreciated.

Let's talk about something that I think a lot of people get backwards when it comes to prompt engineering. We're constantly trying to get more out of AI. Better answers. Better reasoning. Better formatting. More detail. More consistency. More creativity. And what do we do? We add another instruction. Another constraint. Another paragraph explaining what we don't want. Another rule to stop the AI doing something it did three conversations ago. Before long, we've created a prompt so complicated that maintaining it becomes a job in itself. And here's the question I want you to consider. What if the problem isn't that you're asking too much of AI? What if the problem is that you're not giving it enough of what it actually needs? There's a difference. Think about hiring somebody to do a job. You could give them a twenty-page instruction manual explaining every possible thing they might encounter. Or you could explain what you're trying to achieve, give them the information they need, establish what a good result looks like, and let them get on with it. That's the distinction we're exploring today. Because good prompt engineering isn't about squeezing every possible capability out of a language model. It's about designing an interaction where the model has enough information to produce something useful. And that brings us to the architecture of a standing prompt. A standing prompt is essentially the foundation you establish before the individual task begins. It's the persistent instruction set that tells an AI how to work with you. But here's where people get carried away. They start trying to account for absolutely everything. Every possible scenario. Every possible mistake. Every possible formatting preference. Every possible response they might dislike. And the prompt grows. Then it grows again. Eventually, you've got a collection of instructions that contradict one another, overlap, or compete for attention. This is over-optimisation. And it's particularly interesting because the intention is usually good. You're trying to make the system more reliable.