UK edition
Topic
Plain-English explainers on AGI, superintelligence, ASI, alignment, evals and frontier AI terms, written from the UK for a reader who is searching.
A system prompt is the hidden block of instructions that sets a model's role and rules before you type a word. Here is what it is, how it differs from your prompt, why it is not a secret or a security control, and how it connects to prompt engineering and prompt injection.
An AI wrapper is a product built on top of a model someone else trained, and 'just a wrapper' has become an insult. Here is what the term really means, the no-moat critique behind it, and the four things, data, workflow, distribution and trust, that separate a thin wrapper from a defensible product.
A vector database stores embeddings, the numerical fingerprints AI models make for text and images, and finds the ones closest in meaning to a query. That nearest-neighbour search is what powers semantic search and RAG. Here is what it does, and when a library or a Postgres extension will do the same job.
Transformers are the architecture underneath almost every modern AI model, and the word for how they work, attention, is widely misunderstood. Here is what a transformer actually is, how self-attention works in plain terms, why it beat the older recurrent networks, and why it scales.
A context window is how much an AI model can hold in mind for a single request, and its size is now a headline selling point. Flagship models have reached about a million tokens. But research on 'lost in the middle' and 'context rot' shows a bigger window does not mean the model reliably uses all of it.
Fine-tuning is one of the most misunderstood levers in AI. It changes how a model behaves, its format, tone and consistency, not what it knows. Here is what fine-tuning actually does, how the cheap version called LoRA works, and the rule for when to fine-tune instead of reaching for retrieval or a longer prompt.
Everyone has an opinion on prompt engineering, few can define it. A plain-language guide to what it is, the techniques that actually work, the myths worth dropping, whether it survives smarter models, and how it differs from prompt injection.