Prompt Engineering•10 min read
Prompt Engineering Best Practices: Getting More from Your LLM
Discover advanced prompt engineering techniques including few-shot learning, chain-of-thought prompting, and optimization strategies. Learn how to reduce costs, improve accuracy, and get consistent results from your LLM applications.
Prompt Engineering Best Practices: Getting More from Your LLM
Effective prompt engineering is both an art and a science. Well-crafted prompts can dramatically improve the quality, consistency, and cost-effectiveness of your LLM applications.
Key Principles
- Be Specific: Vague prompts lead to vague results. Clearly define what you want.
- Provide Context: Give the model relevant background information.
- Use Examples: Few-shot learning can significantly improve results.
- Iterate: Test and refine your prompts based on actual outputs.
Common Patterns
- Chain-of-Thought: Asking the model to think step-by-step
- Role-Based: Assigning the model a specific role or persona
- Structured Output: Requesting responses in specific formats (JSON, markdown, etc.)