Get Started
Prompt Engineering
- Introduction
- Model Basics
- Prompt Structures
- Clarity & Specificity
- Using Context
- Role Instructions
- Step-by-Step
- Handling Ambiguity
- Creativity vs Precision
- Using Examples
- Advanced Techniques
- Troubleshooting
- Common Pitfalls
- Evaluating Quality
- Real-World Examples
- Prompt Templates
- AI Tasks
- Safety & Ethics
- Multimodal Prompts
- Data Extraction
- Conversation
- Personalization
Common Pitfalls and How to Avoid Them
Even experienced users make mistakes! Here are some common pitfalls and how to avoid them. Recognizing these issues early can save you time and improve the quality of your results.
Common Mistakes
- Being too vague or too broad: The AI doesn't know what you want, leading to generic or irrelevant answers.
- Forgetting to specify format or length: The output may be too long, too short, or in the wrong format.
- Overloading the prompt with too many instructions: The AI may get confused or focus on the wrong part of the task.
- Ignoring the model's limitations: Expecting the AI to know things it can't, or to perform tasks outside its capabilities.
- Not providing enough context: The AI needs background information to give relevant answers.
Examples of Pitfalls
Vague Prompt:
Tell me about history.Improved Prompt:
Write a 3-paragraph summary of the causes and effects of the Industrial Revolution, focusing on its impact on European society.Review your prompts for clarity, specificity, and focus before using them. If possible, test your prompt and revise based on the output.
Best Practices for Avoiding Pitfalls
- Be specific about your intent, format, and audience.
- Break complex tasks into smaller, manageable prompts.
- Test and iterate: Try your prompt and refine it based on the results.
- Stay aware of the model's strengths and weaknesses.