Prompt Best Practices
Best practices for prompt engineering apply to both the system message and the prompts you send to the AI agents.
1. Put instructions at the beginning of the prompt and use markdown headers or code blocks to separate the instruction and context
Less effective ❌:
Better ✅:
2. Be specific, descriptive and as detailed as possible about the desired context, outcome, length, format, style etc
Less effective ❌:
Better ✅:
3. Articulate the desired output format through examples
Less effective ❌:
Better ✅:
4. Try using zero-shot and few-shot examples
Use zero-shot for simple tasks and few-shot for more complex ones. If neither work, consider fine-tuning the model.
Example of Zero-shot:
Example of Few-shot:
5. Reduce “fluffy” and imprecise descriptions. Be clear and succinct in your instructions.
Less effective ❌:
Better ✅:
6. Instead of just saying what not to do, say what to do instead
Avoid negative instructions, and provide clear guidance on what actions should be taken.
Less effective ❌:
Better ✅:
7. Code Generation Specific - Use “leading words” to nudge the model toward a particular pattern
Less effective ❌:
In this code example below, adding “import” hints to the model that it should start writing in Python. (Similarly “SELECT” is a good hint for the start of a SQL statement.)
Better ✅:
Resources
OpenAI, Best practices for prompt engineering with OpenAI API
Prompt Hub, 10 Best Practices for Prompt Engineering with Any Model