Prompting Foundations
The bedrock skills of prompt engineering — how models read your words, the anatomy of a strong prompt, and how to steer tone, format, and accuracy.
Steering tone, length, and accuracy
Small instructions, big control Once your prompt has the four parts, a few extra instructions give you fine control. Tone Name the voice you want: “warm and encouraging”, “concise and formal”, “plain English, no jargon”. You can even anchor it: “in the style of a helpful colleague, not a press release”. Length Be specific: “under […]
Read the answer like a diagnosis
A disappointing answer is evidence, not a dead end The most common mistake after a bad reply is to delete the prompt and type a different one from scratch. That throws away the only useful information you just bought: the specific way it went wrong. Almost every disappointing answer fails in one of a handful […]
Showing beats explaining: examples
When describing what you want stops working Some requirements are easy to describe: “under 100 words”, “in a table”, “no jargon”. Others are almost impossible — house style, the particular way your team writes a bug title, the voice of your newsletter. You know it when you see it, and so does the model, but […]
Where models are strong and where they are not
Knowing when not to ask is part of the skill A language model predicts fluent text. Everything it is good at, and everything it is bad at, follows from that one fact — and fluency is not the same thing as being right. Reliably strong Transforming text you supply — summarising, rewriting, translating, changing register, […]
Build your own prompt checklist
Turning this course into something you actually use Technique you have to remember is technique you will skip when you are busy. The fix is a short checklist you keep beside you until it becomes automatic — the same reason surgeons and pilots use them, and for the same reason: not because the steps are […]