Most people use AI like they'd use Google — short, keyword-based queries. "Summary of WW2." "Fix this code." "Marketing ideas." The results are mediocre and they blame the AI.
The problem isn't the AI. It's the prompt.
Give it a role
Start your prompts by telling the model who it's being. Not because it changes its personality, but because it activates the right cluster of patterns in its training data.
*Bad:* "Write a tagline for my app."
*Better:* "You are a senior copywriter who specializes in B2B SaaS. Write 5 punchy taglines for a tool that helps developers write better commit messages."
Be specific about output format
If you want a list, say "give me a bulleted list." If you want short, say "under 50 words." If you want a table, say "format this as a markdown table." Models default to paragraph form unless told otherwise.
Zero-shot vs few-shot
Zero-shot means no examples — just the instruction. Works fine for common tasks.
Few-shot means including examples of what you want before asking. Dramatically better for specific formats, tones, or edge cases:
Classify this customer feedback as Positive, Negative, or Neutral.
"The onboarding was a bit confusing but the product is great" -> Positive
"I've been waiting 3 days for a refund" -> Negative
"The update landed today" -> Neutral
"Your support team resolved my issue in under 5 minutes" ->The system prompt
If you're using the API (or a tool that exposes system prompts), this is where you set permanent context. Think of it as the briefing you give before every conversation. A good system prompt defines: role, constraints, tone, and what to do when uncertain.
The single most important thing
Write more. The biggest mistake beginners make is being too brief. AI doesn't get annoyed by detail. It performs better with it. More context, more constraints, more examples = better output. Always.