Standard Practices for Text Generation
Part of the Prompt Engineering course
Lessons two and three gave you a feel for how the model actually produces text one token at a time, and the five principles for steering it. This lesson turns that understanding into a small set of habits that make text generation reliable enough to build on. These are the conventions practitioners reach for every day, and once they are second nature your prompts stop surprising you.
When your prompt contains both an instruction and a chunk of text to act on, the model has to figure out where one ends and the other begins. If you just run them together, it can misread part of your data as a new instruction, or fold your instruction into the thing it is supposed to be summarizing. ...
Want to learn Prompt Engineering Standard Practices for Text Generation in a real environment?
Click below, get a fresh Linux box with Prompt Engineering ready to go, and work through Standard Practices for Text Generation hands-on with an AI tutor that knows your code.
Start this lessonStandard Practices for Text GenerationWhat you'll learn
- Prompt formatting and delimiters
- System, user, and assistant roles
- Few-shot prompting
- Parsing structured output like JSON
Read this lesson in your language
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
