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.