Prompt Engineering
The practice of crafting inputs to AI models to get better, more consistent, and more useful outputs.
Why Prompting Matters
The same AI model can give wildly different results depending on how you ask. A vague prompt gets vague answers. A specific, well-structured prompt gets useful responses.
This isn't magic. It's about communicating clearly with a system that takes instructions literally.
Core Prompting Techniques
Be specific. "Write a short email" vs "Write a 3-sentence email declining a meeting politely."
Provide context. Tell the AI who the audience is, what tone to use, what constraints exist.
Give examples. Show what good output looks like. AI models are great at pattern matching.
Structure your request. Break complex tasks into steps. Use clear formatting.
Set constraints. "Do not include technical jargon" or "Keep responses under 100 words."
Advanced Techniques
Chain of thought: Ask the AI to explain its reasoning step by step. This often improves accuracy for complex tasks.
Role playing: "You are an experienced software architect reviewing this code."
Few-shot learning: Provide several examples of input-output pairs before your actual request.
Prompting vs Other Approaches
Good prompting can often achieve what people think requires fine-tuning. Before investing in custom models, exhaust prompting options.
However, if you're making the same complex prompt repeatedly, consider:
- Saving it as a template
- Fine-tuning to bake the behavior in
- Using tools with built-in prompting