Shelf three · Language models & generative AI
Prompting & Context
Kitchen table
How you ask changes what you get – same as with people. 'Help me with my homework' gets you a shrug. 'Here is the question, here is what I tried, here is where I got stuck' gets you real help. The machine also has a limited desk: it can only look at so many pages at once, so choosing what to put on the desk is most of the game. Give it the right facts and it is brilliant. Give it nothing and it guesses – confidently.
Whiteboard
Instructions, examples, retrieved documents, tool results and conversation history all compete for one finite context window, and the model attends unevenly across it – order matters, the middle gets lost, and everything you leave out is a fact the model does not have. Compaction, summarisation and retrieval-on-demand are design decisions, not conveniences. 'Prompt engineering' matured into context engineering: less magic words, more information architecture for a reader with no memory of the last request.
Boardroom
Most disappointing AI results are not a model problem. They are a context problem: the system was never shown the policy, the price list or the customer's history it needed. That reframing changes where the money goes: away from waiting for a better model, toward the unglamorous work of connecting your own information so it arrives at the right moment. When a pilot underwhelms, ask 'what did the model actually see?' before asking 'do we need a bigger model?' The first question is usually the answer.
Related concepts