When thinking about generative AI and its disruptive impact, text generation often comes up as the most representative example of this new chapter in technological advancement. Large language models (LLMs) are rapidly transforming sectors that have at their core text generation tasks such as writing, drafting or summarisation, and the online news industry has been challenged in adapting to these new tools since GPT (generative pre-trained transformer) models became known to the mass public in late 2022.

Imagine you would like to write an essay about quantum computing, but your knowledge about quantum computing comes only from your high school textbooks. In this case, you know how to write good papers, but your knowledge is limited. Now imagine if you could access any library in the world while writing. That would make your work easier, and it’s essentially what retrieval-augmented generation (RAG) does for large language models (LLMs).

As the academic field increasingly implements technology to assist with output, LLMs have moved to centre stage, proving they can serve as powerful co-pilots, assisting with understanding abstract concepts, ideation, language-based prototyping, documentation and communication across disciplines. In educational settings, particularly with design students, LLMs have great potential as Creativity Support Tools (Frich Pedersen et al., 2018) and design material (Yu, 2025).

“Is it fair?” “Is it explainable?” “Is it safe?” These are the questions commonly used to evaluate AI systems. In mental health, they seem especially relevant. Ethical guidelines, audit tools and compliance checklists promise trustworthy AI. But how helpful are these tools when emotional nuance and personal vulnerability come into play?

Imagine an LLM tailor-made for your cultural context such as where you live, the language you speak and the values most important to you. You can depend on it to proofread your emails for tone and social faux pas, respond in everyday, colloquial language and give you relevant recommendations on how to navigate your relationships. You can trust it not to give awkward responses that could put you in a compromised position and to understand the subtle nuances in human interaction vital to help you navigate everyday life.

Have you ever wondered how ChatGPT understands your sentences? The answer is hidden in two mathematical tricks that seem more complicated than they are.

Imagine this: You’re texting your friend about weekend plans. You type “Let’s meet at the park tomorrow” and send it. This is simple for you and your friend, right? But if AI were reading this message, it would be translated to something it can actually work with – numbers. Lots of these numbers format into what we call vectors.

Here we are in the world of embeddings and positional encoding, where words become vectors and positions, essential elements for AI to understand human languages.