Prompting Best Practices: Write Clear Instructions

Prompting Best Practices: Write Clear Instructions

Nov 24, 2025 - 4 Min read

The Foundation of Good Prompts

The difference between a helpful AI response and a useless one often comes down to one thing: how clearly you stated what you want.

AI models are powerful, but they’re not mind readers. Vague prompts get vague results. Clear instructions get useful outputs.

Be Specific About What You Want

Vague: “Help me with this code”

Clear: “Review this Python function for potential bugs and suggest improvements for error handling”

Vague: “Write something about our product”

Clear: “Write a 200-word product description for our project management tool, targeting small business owners, emphasizing ease of use”

The specific prompt tells the AI exactly what to do, for whom, and with what constraints.

Include Context

AI doesn’t know what you know. Provide relevant background.

Without context: “Why is this slow?”

With context: “This SQL query runs against a PostgreSQL database with 10 million rows. It takes 30 seconds. The table has an index on customer_id. Why might it be slow?”

Context transforms a guessing game into useful analysis.

Specify the Format

Tell the AI how you want the output structured.

Unstructured: “Give me some ideas for improving our onboarding”

Structured: “Give me 5 specific, actionable ideas for improving our user onboarding. For each idea, include: the change to make, why it would help, and how to measure success.”

Format specifications prevent you from having to ask follow-up questions.

Define the Audience

Who is this for? Technical level matters.

For developers: “Explain how OAuth 2.0 authorization code flow works, including the token exchange process”

For executives: “Explain OAuth 2.0 in business terms—what problem it solves and why we should use it instead of building our own authentication”

Same topic, different explanations based on audience.

Set Constraints

Constraints improve output quality by forcing focus.

Unconstrained: “Write about machine learning”

Constrained: “Write a 500-word introduction to machine learning for business analysts. Focus on practical applications in retail. Avoid technical jargon.”

Constraints aren’t limitations—they’re guardrails that produce better results.

Use Examples

Show what you want, not just tell.

Without example: “Summarize this document in our style”

With example: “Summarize this document. Here’s an example of our summary style:

‘Q3 showed 15% revenue growth driven by enterprise expansion. Key wins: Acme Corp ($2M), BigCo ($1.5M). Challenges: increased churn in SMB segment (addressed in Q4 roadmap).’

Match this tone and structure.”

Examples are worth a thousand words of instruction.

Break Complex Tasks Into Steps

For multi-part tasks, be explicit about the sequence.

Monolithic: “Analyze this data and give me recommendations”

Step-by-step: “1. First, summarize the key trends in this sales data 2. Then, identify the top 3 underperforming regions 3. For each underperforming region, suggest one specific action 4. Finally, prioritize the actions by expected impact”

Explicit steps produce more thorough, organized responses.

Tell It What NOT to Do

Sometimes exclusions are as important as inclusions.

“Write a product comparison between our tool and Competitor X. Focus on factual feature differences. Don’t make claims we can’t verify. Don’t disparage the competitor—let the features speak for themselves.”

Exclusions prevent common mistakes and unwanted outputs.

Practice: Before and After

Before: “Help me with my email”

After: “I need to email a client who’s upset about a delayed delivery. Write a professional apology email that: acknowledges the delay and its impact, explains what happened without making excuses, offers a specific remedy (10% discount on next order), and confirms the new delivery date (Friday). Keep it under 150 words.”

The “after” prompt will produce a usable email on the first try. The “before” prompt will produce generic advice you can’t use.