The Five Principles of Prompting
Part of the Prompt Engineering course
You already know what a prompt is, what a completion is, and that the model is writing a fresh reply each time rather than reciting something it looked up. That raises the obvious question: if the model is just guessing its way to a plausible-sounding reply, how do you get it to guess toward what you actually want? Every reliable prompt you will ever write leans on the same five moves.
You have the vocabulary now: a prompt goes in, a completion comes back, and the model gets there by predicting one token at a time. What you do not have yet is a method. You can collect a hundred clever phrasings, but phrasings go stale and every task is a little different. What does not go stale is a checklist of the moves that separate a prompt that works from one that guesses. ...
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Start this lessonThe Five Principles of PromptingWhat you'll learn
- Giving direction
- Specifying format
- Providing examples
- Evaluating quality
- Dividing labor
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All lessons in this course
11 lessons- 1What a Prompt Is20 min
- 2How Large Language Models Generate Text30 min
- 3The Five Principles of Prompting30 min
- 4Standard Practices for Text Generation35 min
- 5Prompt Templates and Function Calling35 min
- 6Vector Databases and Retrieval-Augmented Generation40 min
- 7Autonomous Agents: Memory and Tools40 min
- 8How Diffusion Models Generate Images30 min
- 9Standard Practices for Image Generation35 min
- 10Advanced Image Techniques: Inpainting, Outpainting, and ControlNet35 min
- 11Building AI-Powered Applications40 min
