How to Prompt Like a Pro: The FRAME Method for Better AI Results
Why Your Input Matters More Than the Model
Every few months, another frontier model drops. Bigger, faster, sharper. They play leapfrog with one another in performance, and it’s tempting to think the tech itself is the differentiator.
But here’s the real talk: your output is only as good as your input.
AI isn’t magic, it’s leverage. And just like financial leverage or fitness training, it only compounds if you know how to use it well. The gap between a mediocre prompt and a well-framed one is often the difference between generic noise and executive-ready insight.
If you’ve ever been disappointed by AI’s bland or rambling output, it’s not the model. It’s the way you’re speaking to it.
How We Learned to Stop Asking and Start Framing
At SPARK6, we use AI daily for research briefs, market scans, product brainstorming, and summarizing long-form content. Early on, I noticed a pattern: when prompts were vague, results were vague. When we layered in structure, suddenly the outputs were crisp, tailored, and repeatable.
The breakthrough was realizing that a good AI workflow looks more like building a system than asking a question. That’s how the FRAME framework was born. It’s not about one clever trick, it’s a way to consistently debug and refine prompts until they deliver results you can trust.
The FRAME Prompting Method
F → Function
Giving the AI a role sets its voice, expertise, and point of view. Without it, outputs default to bland middle-of-the-road answers. With it, you direct how the AI should think before it even begins.
Weak: “Summarize this article.”
Strong: “You are a research analyst preparing executive summaries for busy CEOs.”
R → Rules
Rules are guardrails. They limit rambling, jargon, and filler, and they ensure the output matches the shape you need.
Example: “Summarize into 5 bullets, max 20 words each. Use plain language, no filler.”
A → Actions
Actions are the job description. If you just say “do X,” the AI will guess. If you spell out the task, the results come back focused.
Example: “Summarize this article into an executive briefing that highlights key takeaways for decision-making.”
M → Materials
AI defaults to generic unless you feed it context. Supplying audience, purpose, or examples shapes the response around your specific reality.
Example: “This is for a CEO preparing for a strategy meeting. Here’s a past summary: Revenue dropped 12% due to churn…”
E → Expected Output
The same content can be delivered a hundred ways. If you don’t define the structure, you’ll often get a wall of text. Specifying format gives you copy-paste-ready results, whether it’s a table, structured essay, or markup language.
Example: “Deliver as a bulleted list with bold headers for each takeaway.”
The Past Tense of FRAME is Where the Magic Happens
Models will keep leapfrogging each other. Some will be slightly smarter, others faster, others cheaper. But the biggest performance upgrade available right now is on your side of the keyboard.
Prompting is a skill. FRAME is how you train it.
And here’s the kicker: the past tense of FRAME is “FRAMED.” That’s where the D comes in: Debugging.
Because the first draft prompt is rarely the best one. The magic is in refining: cutting wordiness, sharpening context, clarifying role. Debugging is how you go from one-off good answers to a repeatable system you can trust.
The Secret Power of Debugging Your Prompts
The real power comes when you refine.
If output feels too generic → Add more Materials (context, examples, audience).
If too long or wordy → Tighten the Rules (word count, bullet cap).
If it misses the point → Refine the Action (for example, “focus on financial implications”).
If tone feels off → Adjust Function (role) or style.|
If it’s strong → Save it. Build a library of FRAMEd prompts you can reuse — or better yet, save all those base instructions as custom GPTs, Gems, etc.
Over time, you stop reinventing the wheel and start pulling from a system of proven templates. That’s how you turn AI from a novelty into a reliable teammate.
”Context is worth 80 IQ points.” -Alan Kay
Find your next edge,
Eli
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