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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
Troubleshooting and Iterating Prompts
If your prompt isn't working as expected, don't worry! Iteration is part of the process. Troubleshooting helps you identify what went wrong and how to fix it, leading to better results over time.
Common Issues and Solutions
- Ambiguity: The prompt is too vague. Try adding more detail or specifying the format.
- Missing context: The AI doesn't have enough information. Include relevant background or examples.
- Unexpected output: The model misunderstood your intent. Rephrase or break the task into smaller steps.
Troubleshooting Steps
- Check for ambiguity or missing context. Ask yourself if the prompt could be interpreted in more than one way.
- Try rephrasing or adding detail. Be explicit about what you want.
- Test with different phrasings to see what works best.
- Use examples to clarify your expectations.
- Iterate: Make small changes and observe the results.
Prompt engineering is an iterative process—experiment and refine for best results. Don't be afraid to try new approaches or ask for feedback from others.