Why Simple AI Prompts Beat Fancy Frameworks (And What Actually Works)
Every few weeks, someone shares a “revolutionary” new prompting technique in one of my communities. Last week, it was something claiming to be from MIT—recursive metacognitive reasoning with confidence scores and self-reflection loops. Supposedly improves AI responses by 110%.
I’ve been building AI agents for clients since 2023, and I’ll be honest: I laughed.
Not because the underlying ideas are wrong. But because the packaging makes everything sound way more complicated than it needs to be.
Here’s what you actually need to know:
→ Great prompts come down to five core elements that haven’t changed in two years
→ Complex frameworks often add cognitive load without improving results
→ Most people struggle with prompts because they’re writing three sentences when they need three paragraphs
→ Your AI business team members need the same clear communication you’d give any team member
What most people miss? The fanciest prompt framework in the world won’t fix a prompt that’s missing basic information. It’s like trying to use a spell-checker on a document that doesn’t exist yet.
This article covers how to write prompts that actually work—the approach I use every single week building AI agents for coaches and course creators. No PhD required, no academic papers to study, no special syntax to memorize.
The Problem With “Advanced” Prompting Frameworks
I get why these frameworks keep popping up. A lot of people genuinely struggle with prompts. They type in a few sentences, get mediocre results, and assume they need something more sophisticated.
So when someone shares a framework with impressive-sounding names—metacognitive chain of thought density, recursive self-reflection scoring—it feels like the answer.
Here’s what happened when I asked one of my trained AI agents to evaluate that MIT framework everyone was sharing. The response was refreshingly honest: “The core ideas are real. The packaging is hype.”
Breaking problems into steps? Yes, that helps. Prompt engineers have been doing this for years. But the confidence scores from 0 to 1? My agent admitted it can output those numbers, but they’re “performative.” There’s no actual calibrated probability happening behind the scenes—it’s pattern matching with what sounds right.
In plain English: the AI is giving you vibes, not statistical precision.
The bigger issue is that these frameworks create a false sense of security. People think if they just follow the magic formula, they’ll get perfect results. But prompting isn’t about finding the right incantation. It’s about clear communication.
When you hire a new team member, you don’t hand them a complicated framework and hope for the best. You tell them who they are on your team, what you need done, how you want it done, and what to avoid. AI works the same way.
The Five Elements That Actually Make Prompts Work
I just spent an afternoon building two new AI agents for a client. These agents work—not because I used some metacognitive framework, but because I included five things that every solid prompt needs.
A Clear, Specific Role
“You are a helpful assistant” is useless. The AI has no idea what to actually do with that.
Compare that to: “You are a strategy session architect who converts course competencies into bookable strategy session offers.”
See the difference? The AI immediately knows what it’s supposed to be. It has context. It has a job description.
When I’m helping clients build their AI business team, this is usually the first thing we fix. Most people skip the role entirely or make it so vague it might as well not be there.
Specific Tasks With Boundaries
What’s in scope? What’s out of scope?
For the agent I built today, I was explicit: “You write the session name and the Calendly copy. You don’t do pricing. You don’t do scheduling. You don’t do sales scripts.”
When you draw that box, AI stays inside it. Skip this step, and you’ll end up with an agent that wanders into territory you never asked about—sometimes territory that creates more work for you to fix.
Think of it like training a really good assistant. You wouldn’t just say “help me with my business.” You’d say “I need you to handle client intake calls on Tuesdays and Thursdays, and you’ll use this script, and you won’t discuss pricing—send those questions to me.”
Step-by-Step Rules
This is where the “chain of thought” idea actually lives. Not in some fancy framework—just in very simple instructions.
The fancier and more complicated you get, the less likely the AI knows what you want. It’s the same with any team member. Simple instructions that follow a clear sequence work better than complex explanations that require interpretation.
Here’s an example from the agent I built:
Step one: Read the business plan and extract the six competencies.
Step two: For each competency, generate two session idea names—one problem-focused, one outcome-focused.
Step three: Write Calendly copy for each.
That’s it. Explicit steps in order. No room for confusion.
Guardrails (What Never to Do)
This is the part most people skip entirely, and it’s one of the most important pieces.
For my client’s agent, I included: Never ask for clarifying questions. Never use generic names like “free consultation.” Never add emojis. Never promise specific results.
Guardrails prevent your AI business team member from drifting into bad habits. Without them, the AI makes assumptions about what you’d want—and those assumptions are often wrong.
I’ve seen agents start adding emojis everywhere because the AI decided that would make content “more engaging.” I’ve seen agents promise “guaranteed results” in copy because nothing told them not to. These are the kinds of outputs that create real problems if you’re not paying attention.
Examples of Good Output
If I had to pick the most overlooked element in prompt writing, this would be it.
When you want the AI to produce something specific, show it what good looks like. Don’t just describe it—give an actual example.
For the strategy session agent, I included a full example: “Here’s a competency called gut health. Here’s a session name called Gut Health Reset. Here’s exactly what the Calendly copy looks like.”
One good example beats a thousand words of instruction. The AI can pattern-match against something real instead of guessing based on your descriptions.
What This Looks Like in Practice
Let me make this concrete with a side-by-side comparison.
A Mediocre Prompt:
“Help me create some strategy session ideas for my coaching business.”
What’s wrong here? No role. No context. No structure. No examples. The AI has to guess everything. And that fancy MIT framework everyone’s sharing? It isn’t going to fix this. If the underlying prompt is incomplete, no “improver” technique will rescue it.
A Strong Prompt:
“You are a strategy session architect. You take a one-page business plan with six competencies and create 12 strategy sessions—two per competency—with Calendly-ready marketing copy.
Each session name should be two to six words, either problem-focused or outcome-focused.
Each description should be 75 to 150 words with:
→ A hook
→ Three to four bullet points on what you’ll cover
→ A qualifier (who it’s for)
→ A soft call to action
Never ask questions—just deliver the output.
Here’s an example of what good looks like: [include full example]”
Same task. Completely different result.
The second prompt works because it’s specific, structured, and complete. Not because I used some metacognitive framework with confidence scores.
When to Add Complexity (And When to Keep It Simple)
I’m not saying complex prompts are always bad. Sometimes you genuinely need sophisticated logic, conditional branching, or multi-step reasoning chains.
But here’s the test I use: Can I explain what this prompt does in one sentence?
If I can’t, it’s probably too complicated. Or I don’t understand my own goals clearly enough yet.
Most coaches and course creators I work with don’t need PhD-level prompt engineering. They need AI agents that reliably produce content in their voice, create consistent outputs, and save time without creating more cleanup work.
For that, the fundamentals work. Clear role, specific task, step-by-step rules, guardrails, examples.
Where I add complexity is when the task itself is complex—like an agent that needs to handle multiple scenarios differently, or one that needs to integrate information from several sources before producing output. But even then, I break it down into simple, sequential steps rather than abstract frameworks.
Getting Started With Better Prompts
If you’re building AI assistants for your business and getting inconsistent results, start here:
First, check your role definition. Does your AI know exactly what job it’s filling? “Content writer who creates LinkedIn posts for health coaches” is infinitely better than “helpful assistant.”
Second, define what’s in scope and out of scope. What should this agent handle? What should it absolutely not touch? Write both explicitly.
Third, break your instructions into numbered steps. Don’t describe the process—list it. First this, then this, then this.
Fourth, add your guardrails. What should this agent never do? Be specific. “Never promise specific results” is better than “be careful with claims.”
Fifth, include at least one example. Show the AI what a great output looks like for your specific situation.
You don’t need to study academic papers. You don’t need that MIT-released prompt fixer. You just need to start telling AI exactly what you need, how you want it, and what you don’t want.
Specificity beats complexity every single time.
The Bigger Picture
The fundamentals of great prompting haven’t really changed since I started doing this work. It’s the same as the fundamentals of good communication with any team member—be clear, be specific, set expectations, provide examples.
What keeps changing is the packaging. New frameworks with impressive names that sound like they’ll solve everything. And look, I get why people chase them. When you’re struggling to get good results from AI, a framework that promises 110% improvement sounds appealing.
But the coaches and course creators I work with don’t have time to become prompt engineering researchers. They need systems that work reliably so they can focus on what they actually do—coaching, creating courses, serving their clients.
That’s what these fundamentals provide. Not magic, not overnight expertise, but a solid foundation that produces consistent results.
The agents I build for clients work because I’m specific about what I need—not because I’ve discovered some secret technique. And honestly, that’s good news. It means you don’t need anything special to build AI assistants that actually help your business.
You just need to communicate clearly. Which, if you’re a coach or consultant, you’re already pretty good at.
