Have you ever been in a noisy place, perhaps a party or a café, yet you manage to filter all the background noise and focus on a single conversation? This human ability to concentrate on what is important while ignoring distractions is something AI models now mimic, thanks to a clever trick called the “attention mechanism”.

If you have been following the rapid rise of large language models (LLMs), you have likely also run into retrieval-augmented generation (RAG) systems. When we need an LLM to answer domain-specific questions without hallucinating, we hook it up to a library of documents so it can search for the right context before answering. It is a highly effective way of “teaching an LLM to use a library”, as previously explained in another blogpost.
But once you build a RAG system, a critical question emerges: How do you actually know if it is working well?

A student sits down to write an essay, solve a programming exercise or prepare for an exam. Instead of searching through textbooks or waiting for office hours, they open an AI assistant. The system offers explanations, suggests what to try next and responds when the student asks follow-up questions. In many ways, this looks like a breakthrough for education: support is available at any time, at low cost and in a conversational form.

In the film Humans in the Loop, there is a scene with a woman named Nehma, who works as a data labeller in a rural data-labelling centre in India. Nehma is tasked with annotating images of insects so that an AI-powered agricultural machine can identify and target them. Her American client needs her to label these insects as pests. When shown an image of a leaf-eating caterpillar, Nehma refuses to mark it as a pest. Drawing on traditional ecological knowledge, she explains that the caterpillar only eats the decaying parts of the leaf, allowing the rest of the plant to survive and thrive. Her manager tells her to follow the guidelines strictly, rather than applying her own understanding of what counts as a pest.

In 1956, researchers Horton and Wohl described something they had noticed about television audiences: people were forming genuine emotional bonds with personalities they had never met and never would. They felt affection, familiarity, even concern for figures who had no awareness of their existence. Horton and Wohl called these parasocial relationships and at the time the phenomenon was mostly treated as a curiosity of broadcast media.

When the Terminator films first depicted Skynet becoming self-aware, the moment was presented as a turning point not only for humanity, but for the idea of intelligence itself. In the film, this “self-awareness” meant more than simply processing information or adapting to input. It signified the recognition of one’s own existence, the ability to formulate independent goals and the emergence of a self-preservative instinct.