The Art & Science of Prompt Engineering
Discover how small changes in your prompts can dramatically improve AI responses.
Core Techniques
- Zero-shot prompting — Direct the AI without examples
- Few-shot learning — Provide examples of desired responses
- Chain-of-Thought — Guide models through reasoning steps
- Role prompting — Assign an expert role to the AI
Advanced Strategies
- Meta-prompts — Prompts that help the AI understand how to interpret your requests
- Context refinement — Iteratively improve prompts based on AI feedback
- ReAct prompting — Reasoning and acting to solve complex problems
- Tree of Thoughts — Exploring multiple reasoning paths
Latest Trends
- Multimodal prompting — Working with text, images, and other formats
- Constitutional AI — Using prompts to guide AI within ethical boundaries
- Auto-prompting — AI systems that optimize their own prompts
- Instruction tuning — Fine-tuning models to follow specific instructions
Prompt Comparison Lab
Compare two different prompting approaches side-by-side to see how they affect AI outputs.
Enter your prompt here:
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Parameter Guide
Temperature
Controls randomness and creativity in responses:
- Low (0.1-0.4): More deterministic, consistent, and focused responses
- Medium (0.5-0.8): Balanced creativity and coherence
- High (0.9-2.0): More creative, diverse, and unexpected outputs
Max Tokens
Sets the maximum length of AI responses:
- Low (100-500): Brief, concise responses
- Medium (1000-2000): Detailed explanations
- High (3000+): Comprehensive, in-depth responses
Top P (Nucleus Sampling)
Controls diversity by limiting token selection:
- Low (0.1-0.5): Limited vocabulary and more predictable responses
- Medium (0.6-0.9): Balanced vocabulary selection
- High (0.95-1.0): Wider vocabulary range for more diverse outputs