Prompt Engineering and the Illusion of Instruction
We think we’re giving orders. We’re steering a next-token engine. Prompts work when they mirror patterns the model has seen. Tiny phrasing changes flip outcomes. Guide with short roles, small examples, and simple formats. Verify. Cooperation, not command.
Sorting Words, Clustering Thoughts
We sort words without thinking. Machines can't. They learn to draw boundaries and find themes: spam or not, tickets by topic. Use labels when you need decisions and clusters when you need discovery. Start there. Patterns come first, meaning follows.
Inside the Clockwork of an AI’s Mind
Ask a question, get a fluent answer. Under the hood, no insight, just a fast loop picking the next token, guided by attention and a tiny memory. See the gears, not a ghost. When you learn the strings, you know when to trust it and when to steer.
Chopping Language, Weaving Meaning
Language models don’t read like we do. They slice text into tokens and map them to vectors. Meaning becomes pattern, not understanding. Learn the quirks of tokenization and embeddings to write tighter prompts, spot bias, and know what gets lost.
The Erosion of Virtue
Speed is prized; thoughtfulness gets labeled “overthinking.” But 深思熟虑, think deeply and act with care, is not delay. It is judgment. Pause for choices that matter. Ask who benefits from the rush. Measure twice, cut once. Finish better, regret less.
