Few-Shot Prompting Patterns That Improve Accuracy in Large Language Models
Few-shot prompting improves large language model accuracy by 15-40% using just 2-8 examples. Learn the top patterns, when to use them, and how they outperform zero-shot and fine-tuning in real-world applications.
Chain-of-Thought in Vibe Coding: Why Explanations Before Code Work Better
Chain-of-Thought prompting improves AI coding by forcing explanations before code. Learn how asking for step-by-step reasoning cuts bugs, saves time, and is now the industry standard for complex tasks.
Comparative Prompting: How to Ask for Options, Trade-Offs, and Recommendations from AI
Learn how comparative prompting transforms AI from a search tool into a decision partner by asking for structured comparisons, trade-offs, and recommendations based on your specific criteria.
Measuring Prompt Quality: Rubrics for Completeness and Clarity
Learn how to measure prompt quality using structured rubrics that evaluate completeness and clarity. Discover the best types, common mistakes, and how to build your own for better AI results.
