Rag

Imagine asking your policy system a simple question and getting a perfect answer in under a second. Now imagine asking it to analyse fifteen years of reports, submissions, evaluations, and operational metrics. One requires a chatbot. The other requires an engine. Policy teams often reach for RAG because it looks neat and tidy. A chatbot that can trawl internal guidance, regulatory texts, consultation documents and memos feels like a small miracle. And to be fair, a well-tuned Retrieval-Augmented Generation system is a brilliant librarian. It fetches the right paragraph, summarises dense text, and saves you from spelunking through document drives that haven’t been organised since John Key was in office. ...

In the world of AI, context isn’t just helpful—it’s everything. Whether it’s the billions of tokens in a language model’s attention mechanism, the rapidly shifting mosaic of user intent, or the structured metadata keeping facts grounded, context defines what AI can do. 1. A Foundation Built on Attention The 2017 paper “Attention Is All You Need” introduced the Transformer model, which revolutionized deep learning by letting models process all parts of a sequence in parallel using self-attention. No more recurrences—just powerful, context-aware computation. Since then, it’s become the backbone of modern models like GPT, BERT, and beyond. ...