1d • 🤑 Ai Money Prompts
Best practices to design prompts
You've seen that user prompts can be unpredictable and are always goal-driven. So how do you work with that variability? By understanding what makes a user prompt effective.
The best practices below will help you design clearer prompts so the AI agent can focus on reaching its goal.
🎯 Be specific about the task
Tell the AI agent exactly what your desired outcome is for the task.
🔎 Example: "Search our CRM for all customers who purchased in Q4 and generate a 150-word retention strategy for each."
#️⃣ Put your request at the beginning of the prompt and include separators
When your prompt includes both instructions and background information, use separators like ### to make the distinction clear. This helps the AI agent understand the task and what output you want before reading any background information.
🔎 Example:
"Identify the main themes in this customer feedback:
###
'The product is great but shipping took forever.'
###"
ℹ️ Provide relevant context for the task
Give background that helps the agent prioritize what matters for the specific request.
🔎 Example: "Generate a weekly performance report for the sales team showing lead conversion rates and top performers."
🔑 Use leading words
Start your prompt with specific keywords or phrases that signal what type of output you want. This guides the AI in the right direction immediately.
🔎 Example: "Evaluate vendor proposals..." or "List the top 5 cities to live in."
✍🏻 Show examples of desired output
Providing examples of the desired result for the task can significantly improve the AI agent's output.
🔎 Example: "Summarize customer feedback like this: [example of customer feedback report]."
✨ Iterate and refine
If the first response misses the mark, add more details to guide the agent closer to what you want.
🔎 Example: "This is helpful, but I need more specific numbers. Add actual metrics and data points from the report I shared earlier."
You don't need to apply all of these best practices for every user prompt. However, the more complex your task, the more these practices matter. Use them when you need precise outputs, when handling multi-step tasks, or when initial responses miss the mark. Think of them as ways to get better results faster, especially for challenging prompts.
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