prompt-engineering
Expert guide for optimizing LLM prompts through patterns, testing, and systematic refinement.
- Covers five core techniques: few-shot learning, chain-of-thought reasoning, prompt optimization, template systems, and system prompt design
- Includes progressive disclosure patterns that scale complexity from simple instructions to multi-example reasoning traces
- Provides practical examples for each pattern, from code review templates to bug analysis workflows
- Emphasizes testing and iteration on diverse inputs, with guidance on measuring performance, avoiding over-engineering, and handling edge cases
Prompt Engineering Patterns
Advanced prompt engineering techniques to maximize LLM performance, reliability, and controllability.
Core Capabilities
1. Few-Shot Learning
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:
Extract key information from support tickets:
Input: "My login doesn't work and I keep getting error 403"
Output: {"issue": "authentication", "error_code": "403", "priority": "high"}
Input: "Feature request: add dark mode to settings"
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