Proven Prompts that actually work

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It’s been 3 years since ChatGPT was released.

I think most of us heard the word PROMPT only after ChatGPT—and only then did we start understanding what it actually does.

Even today, I often wonder why and how to form the right prompt every time for a new task.

And honestly, I’m still facing many problems creating new prompts, task by task.

One of my subscriber from you, Cathie Mequak sent me a Reddit post recently, and it completely changed the way I write prompts daily.

I am sure it will help you too and it’s a much better way than scratching your head every time.

Before that, I need a small favor.

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So here is the Reddit post:

A tech lead who has been truly obsessed with prompts and prompt engineering for the past 3 years spent countless hours analyzing and tracking over 1,000 real-world prompts.

During this process, he discovered something interesting across all successful prompts.

He concluded that there are six common patterns behind them and named this framework KERNEL.

No, not the one we eat.

KERNEL is an acronym for this framework.

It has transformed how I use AI, and I can confidently say it will completely change how you use AI as well. I’m assuring you.

Here is the framework.

K — Keep it simple

• Bad: 500 words of context Good: One clear goal

• Example: Instead of: “I need help writing something about Redis” Use: “Write a technical tutorial on Redis caching”

• Result: 70% less token usage 3× faster responses

E — Easy to verify

• Your prompt needs clear success criteria.

• Replace: “Make it engaging” With: “Include 3 code examples”

• If you can’t verify success, AI can’t deliver it.

• My testing showed: 85% success rate with clear criteria vs 41% without

R — Reproducible results

• Avoid temporal references like: “current trends” “latest best practices”

• Use specific versions and exact requirements.

• The same prompt should work next week and next month.

• My tests showed 94% consistency across 30 days.

N — Narrow scope

• One prompt equals one goal.

• Don’t combine code, documentation, and tests in a single request.

• Split complex tasks into smaller ones.

• Results: Single-goal prompts: 89% satisfaction Multi-goal prompts: 41%

E — Explicit constraints

• Tell AI what not to do.

• Instead of: “Python code” Use: “Python code. No external libraries. No functions over 20 lines.”

• Clear constraints reduced unwanted outputs by 91%.

L — Logical structure Format every prompt like this:

• Context (input)

• Task (function)

• Constraints (parameters)

• Format (output)

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Real example from my work last week:

Before KERNEL: “Help me write a script to process some data files and make them more efficient.”

• Result: 200 lines of generic, unusable code.

After KERNEL:

• Result: 37 lines. Worked on the first try.

Actual metrics from applying KERNEL to 1,000 prompts

• First-try success: 72% → 94%

• Time to useful result: –67%

• Token usage: –58%

• Accuracy improvement: +340%

• Revisions needed: 3.2 → 0.4

Advanced tip:

Chain multiple KERNEL prompts instead of writing one complex prompt. Each prompt does one thing well and feeds into the next.

The best part? This works consistently across GPT-5, Claude, Gemini, and even Llama.