What are the best AI prompts?

The prompts that have made the biggest difference for me aren't clever instruction sets you copy from a list. They're prompts that get better every time you use them.

Compounding Prompts vs. Regular Prompts

A regular prompt gives you a good output once. A compounding prompt updates a file, a rule set, or a memory after each session so the next session starts smarter than the last one.

The difference matters because most people write a prompt, get a result, and start from scratch next time. Compounding prompts build on what happened before.

The concept is simple. Instead of just asking the AI to do something, you ask it to also capture what it learned about how you work, then feed that back into the instructions for next time.

How to Build One

Talk to your AI about your situation first. Describe what you're trying to accomplish, how often you do it, and what frustrates you about the current process. Let the AI help you design the prompt. You don't need to engineer the perfect instruction set yourself. The AI is good at structuring its own instructions when you give it the context.

Three Examples from Different Areas

Content Creation

I run a prompt at the end of every content session that reads through the session, identifies patterns in how I write, what edits I made, what I rejected, and updates a voice guide file. That voice guide becomes the instruction set for all future content.

Session one captures that I prefer colons over dashes. Session five captures that I catch overstatements before the AI does. By session twenty, the AI drafts content that needs only minor cleanup because it learned my preferences across every previous session.

The key instructions:

• Scan the session for drafts, feedback, phrases rewritten, and rules stated during editing
• Extract new rules, voice refinements, what worked, and what needed reworking
• Update the voice guide file so the next session starts with everything this session learned

Software Development

I use a prompt at the end of development sessions that reviews what was built, what decisions were made, what patterns were followed, and updates the project documentation. The next session starts with full context on architecture choices, naming conventions, and lessons from past mistakes.

Instead of re-explaining my codebase every time, the AI already knows why things are built the way they are.

The key instructions:

• Update memory with architectural decisions, bugs fixed, completed features, and file paths that changed
• Document lessons learned: debugging approaches, quirks discovered, patterns that emerged
• Write a handoff summary for the next session with what's completed, what's in progress, and open questions

Productivity and Task Management

After completing any significant task, the AI presents three categories: actions it can handle right now, systems it can set up so the task never needs doing manually again, and messages it can draft for your team.

It compounds by logging which suggestions you actually act on and which you skip. Over time, the suggestions get more relevant because they're shaped by what you've historically found useful, not just what seems logical in the moment.

The key instructions:

• Present next actions, automations to set up, and tasks to delegate with draft messages
• Log which suggestions were acted on and which were skipped
• Use that history to prioritize future suggestions by what you've actually found useful

The Pattern

The pattern across all three is the same. The prompt does the work AND captures something that makes the next round better. That's what separates a prompt you use once from a prompt that becomes part of how you operate.