Prompting Techniques
Proven strategies to dramatically improve AI responses.
Few-Shot Prompting
Give 2-5 examples before the task.
INPUT PROMPT
Chain-of-Thought Prompting
Ask the model to think step-by-step.
INPUT PROMPT
Not all prompts are created equal. Researchers and practitioners have discovered several reliable techniques that push AI models to produce significantly better results. Mastering these techniques is what separates a 'vibe coding practitioner' from a true AI power user.
1. Zero-Shot Prompting
You give the AI a task with no examples. Works great for simple, well-understood tasks.
Example: Translate 'Hello, how are you?' to French.
The AI already knows what 'translate' means and performs the task without needing any sample.
2. Few-Shot Prompting
You provide 2–5 examples before asking the AI to do the task. This is incredibly powerful for formatting consistency or niche tasks.
Example:`
Positive review: 'The food was great!' → Sentiment: Positive
Negative review: 'Terrible service.' → Sentiment: Negative
Neutral review: 'It was okay.' → Sentiment: ???`
The AI learns the pattern from your examples and applies it to the new input.
3. Chain-of-Thought (CoT) Prompting
Ask the AI to 'think step by step.' This forces the model to reason through a problem before answering, which dramatically reduces errors in logic and math.
Magic words: Add 'Let's think step by step.' at the end of any complex question.
Why it works: It forces the model to decompose the problem into smaller, verifiable steps rather than jumping to a final answer.
4. Role Prompting
Assign a specific expert persona to the AI to get domain-specific expertise.
Example: You are a cybersecurity expert specializing in OWASP Top 10. Review this code for security vulnerabilities.
This makes the AI prioritize security-related patterns it has learned.
Combine Techniques
The best prompts combine multiple techniques. A Role + Few-Shot + Chain-of-Thought prompt is almost always better than any single technique alone.