prompt-engineering

原文涵盖的主题

• Prompt Engineering Patterns
• Core Capabilities
• 1. Few-Shot Learning
• 2. Chain-of-Thought Prompting
• 3. Prompt Optimization
• 4. Template Systems
• 5. System Prompt Design
• Key Patterns

Advanced prompt engineering techniques to maximize LLM performance, reliability, and controllability. Teach the model by showing examples instead of explaining rules. Include 2-5 input-output pairs that demonstrate the desired behavior. Use when you need consistent formatting, specific reasoning patterns, or handling of edge cases. More examples improve accuracy but consume tokens—balance based on task complexity. Example: Request step-by-step reasoning before the final answer. Add "Let's think step by step" (zero-shot) or include example reasoning traces (few-shot). Use for complex problems requiring multi-step logic, mathematical reasoning, or when you need to verify the model's thought process. Improves accuracy on analytical tasks by 30-50%. Example: Systematically improve prompts through testing and refinement. Start simple, measure performance (accuracy, consistency, token usage), then iterate. Test on diverse inputs including edge cases. Use A/B testing to compare variations. Critical for production prompts where consistency and cost matter. Example: Build reusable prompt structures with variables, conditional sections, and modular components. Use for multi-turn conversation

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