During a free one-on-one consultation, someone asked me: now, when I just type a prompt, AI sometimes does a good job; but previously, when I wrote detailed skill packages and prompts carefully, the results were not always ideal. What's the difference?
- You've written long prompts or skill packages, but you start to feel they're holding AI back
- You want to keep your own work standards, yet hope AI can suggest more suitable approaches
- You've heard of Loop Engineering, but you still don't know how to connect it with prompts
- Converting old skill packages into intent reference materials
- A directly copyable intent-first prompt
- The minimal process from intent-first to Loop Engineering
Why carefully written prompts sometimes limit AI
Previously, when models weren't mature enough, writing roles, steps, formats, and checking methods clearly could indeed bring results to a usable level. Now that models and tools have advanced, in some tasks, old processes becoming the only rule can prevent AI from using more suitable methods at hand.
Do every step according to the existing method. The advantage is stability, but the cost is that it is difficult to adjust when encountering new tools or new situations.
Keep the standard, tone, and limitations, and let the AI judge the method according to the current task. If you want to change it, first explain the reason and then propose suggestions.
Detailed prompts still have value. What needs to change is its role, from an unchangeable instruction, into data that helps the AI understand how you judge a good result.
Old skill packages are actually your foundation
The processes you wrote down in the past often already contain your standards for results, habitual thinking order, imagined scenes, role tone, and what cannot be touched. These are more valuable than a single instruction.
You can hand over your old skill package to the AI, asking it to first distill your goals and preferences, then divide into requirements that must be retained, methods that can be flexible, and steps that may be outdated.
What to write when prioritizing intention
Intention sounds abstract, but actually you just need to let the AI know a few things. You don't have to write long essays every time, but at least let it see the destination and the boundaries.
What you hope the reader, client, or team will finally know, what kind of result is considered credible, useful, and like you.
What role, tone, and context you want to retain, what data cannot be guessed, and what format must be kept.
Which steps can be adjusted, and if the AI has a more suitable method, how to first explain and report to you.
A directly copyable minimal version
From intention-first to Loop Engineering
For one-time tasks, first clarify the intention, quality standards, and red lines, which is usually very helpful. When encountering tasks that are repeated, have many steps, and require checking results, expand it into Loop Engineering.
Loop Engineering is not just asking AI to run multiple times. It will first clarify the goal, standards, starting point, checkpoints, correction methods, and stopping conditions. Intention-first answers why it is done and to what extent; Loop Engineering then adds how to check in the middle, what to do if it does not meet the standards, and when it can end.
Without intention-first, Loop can easily become running endlessly without knowing where to fix. First clarify the judgment standards, so that you know where to place the checkpoints.
First pick the most commonly used old skill package to try
You do not need to rewrite all the prompt words immediately. First find the most commonly used and most seriously written skill package, and ask AI to help you extract the true intention behind it. Those seemingly old workflows often contain your most irreplaceable judgment force.
Further Reading:Use my article writing workflow to explain what Loop Engineering is. · Don't write fixed instructions: explain clearly what you want, and let the AI handle the rest.
Use AI as a tool to help you accumulate judgment
The questions in this article come from a free one-on-one consultation. I hold two free online lectures every month, with topics rotating around how to turn workflows, judgments, and experience into AI-friendly prompts, skill packages, and knowledge bases. If you want to receive course notifications or want to discuss stuck prompts, you are welcome to start from the community.
The free lecture sessions will be announced here first, and you can also directly upload your old skill package to see how to extract it together.
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