The difference between someone who uses AI effectively six months from now and someone who's stuck at their current level is usually one thing: a regular review practice. AI tools change. Your work changes. The prompts and workflows that worked well last month may underperform today. A weekly review catches drift before it becomes inertia.
Why Weekly Review Matters
Without a review cycle, AI workflows degrade quietly. A prompt that produced excellent output in January produces mediocre output in June — not because anyone made it worse, but because the tasks evolved, the model updated, or the team's expectations shifted. By the time someone notices, the standard has already slipped and the team has adapted to it. Weekly review prevents normalising mediocrity.
The Four-Question Review Template
The review should take 20–30 minutes and answer four questions:
1. What worked better than expected this week?
Identify one or two AI-assisted outputs that exceeded your usual standard. What was different? Was it the prompt? The task type? The context you provided? Document the pattern so you can replicate it. This is how your best practices get codified rather than being happy accidents.
2. What underperformed or failed?
Identify one or two outputs that required significant rework or that you weren't happy with. Trace the failure: was it the prompt? The input? The model? A review step that wasn't sufficient? Knowing the root cause lets you address it specifically — not just "try harder next time."
3. What tasks am I not using AI for that I should be?
As AI capabilities develop, tasks that weren't suitable six months ago may be suitable now. Look at your recurring tasks from the past week and ask: is there any of these where AI could save meaningful time without sacrificing quality? If so, build one test prompt and try it next week. Don't adopt everything — evaluate one new use case per review cycle.
4. What am I using AI for that I should stop, or do differently?
Some AI use is habit rather than value. If a task consistently requires the same level of effort before and after AI involvement — because the output always needs complete rewriting, or the verification always takes longer than drafting would — it might not be the right use case. Cut it or redesign it. Be honest about where the tool isn't helping.
Metrics to Track
Keep a simple log — a shared doc or spreadsheet is fine:
- Tasks assisted: How many tasks used AI assistance this week?
- Time delta: Estimated time comparison on key task types vs baseline. You don't need exact measurement — a rough "about half the time" is useful.
- Quality rating: For outputs that went external, rate quality on a simple scale. Track trend over time, not absolute score.
- Error/rework rate: How many outputs required significant correction? If this increases, investigate why.
You're looking for trends, not precise numbers. A three-month trend line tells you far more than any single week's data.
Updating Your Workflows
The output of a review isn't just notes — it's changes. At each review:
- Update at least one prompt based on the underperformance analysis
- Add one new entry to your prompt library if something worked notably well
- Remove or archive one workflow that's no longer useful
- Set one specific thing to test in the coming week
Small, consistent changes compound. A prompt library that improves by one entry each week has 50 better-than-before entries by year-end. A system that gets reviewed quarterly has four opportunities to improve. The review cadence is the variable that matters most.
Scaling the Practice
If you're leading a team: have each person bring two minutes of notes to a short weekly team sync — one thing that worked, one thing that didn't. Fifteen minutes of collective learning per week prevents duplicate mistakes and spreads best practices without a formal knowledge management system. The structure doesn't need to be complex. It needs to happen consistently.