OpenAI released GPT-5 this month, and the reaction was telling. People were expecting a leap toward superintelligence. What they got was… an incremental upgrade. It was smarter in places, more useful in others. But it wasn’t a breakthrough that suddenly pushed us into the age of Artificial General Intelligence (AGI). That raises a bigger question: what does AGI actually mean, and how would we know if we’d achieved it?

It’s easy to get an AI proof of concept working in isolation. That’s like flying a small remote-control plane in a park. You only need to think about lift-off, flying in circles, and landing without breaking it. Running a production-grade AI system is more like operating an international airport. The stakes are higher. The moving parts are more complex. And you’re responsible for thousands of people depending on you to get them where they need to go.

Most people in AI circles now know what “hallucination” means in the context of language models: it’s when the model confidently invents information that isn’t true. But the word itself — hallucination — brings a whole set of assumptions with it. That the model is trying to tell the truth. That it knows what’s real. That it’s having some kind of human-like mental break. It’s not. This article — the one you’re reading now — is a good reminder of that.

Prompt engineering gets a lot of attention in AI — but most of the content out there focuses on single-agent systems. A user types a query, a model replies. Tweak the prompt, adjust the output. That’s the loop. But that’s not how real AI systems work anymore.

Modern language models can generate incredible responses, but they don't work in isolation. Behind the scenes, the best systems combine large models with fast, context-aware search powered by vector databases. This article walks through what vector databases actually are, how they relate to how LLMs “think,” and why they’ve become critical to making AI systems reliable, explainable, and fast.