Analysis
This article unveils a simple yet powerful technique to significantly improve the accuracy of your Large Language Model (LLM) outputs: explicitly defining negative examples within your prompts. By showcasing what *not* to do, the author demonstrates a clear path to achieving the desired output formats and data structures, opening exciting possibilities for streamlined AI interactions.
Key Takeaways
- •Explicitly providing negative examples in your prompts helps guide the LLM towards the correct output format.
- •This technique is especially useful when specifying format, selection lists, and ID generation rules.
- •The article showcases practical implementation patterns for different scenarios.
Reference / Citation
View Original"The output accuracy improved significantly by adding NG examples."