The most common complaint I hear about AI prompts? 'It didn't do what I wanted.'

Here's what's happening: You're talking to AI the same way you'd talk to a colleague—someone who shares your context, understands your organization, knows what 'better' means in your world. But AI doesn't have that shared reality. It only has what you put in the prompt.

This isn't a limitation you need to work around. It's just a different kind of intelligence that needs a different communication approach. And once you understand that difference, you can structure prompts that get you better outputs faster. Every failed prompt is wasted time. And if you bill clients or value your hours at all, wasted time is wasted money.

Why Your Prompts Feel Like Talking to a Wall

When you ask a colleague for help, you can be vague. They fill in the gaps because they share your context, your industry, your company, your current projects, even the conversation you had yesterday.

AI doesn't have any of that. It only has what's in the prompt.

It processes your request, matches it against patterns in its training data, and generates a response based on probability. No shared context. No memory of your last conversation. No ability to say "I think what you're really asking is..."

The breakthrough comes when you stop expecting AI to work like a human colleague and start structuring your requests to play to its actual strengths: pattern recognition, synthesis, and structured output.

ACTOR: A Framework for Structured Communication

The best way I've found to communicate with AI is through structure. Not because it's rigid, but because it's clear.

Think of it like this: If you walked into a meeting and said "make the thing better," you'd get confused looks. But if you said "I need you to review this proposal as our CFO, identify financial risks, and suggest three ways to reduce costs without compromising quality," you'd get useful input.

AI works the same way. It needs role, context, task, output format, and review criteria.

Here's the structure, written as an acronym to make it easy to remember:

A - Assign Role (The Pattern Activator)

When you tell AI "You are an experienced marketing strategist," you're not playing pretend, You're taking advantage of how large language models work.

Here's the behind-the-scenes reality: Large Language Models are trained on billions of text examples. When you assign a role, you're statistically weighting which patterns get activated. A "marketing strategist" prompt pulls from different language patterns, vocabulary, and frameworks than a "technical writer" or "financial analyst" prompt, even for the exact same task.

Test it yourself: Ask AI to explain the same concept as (1) a marketing strategist, (2) a data scientist, and (3) a kindergarten teacher. You'll get three completely different outputs from the same core information.

C - Context (The Shared Reality Builder)

ChatGPT isn't sitting in the office next to you, I didn't attend your last marketing meeting, or understand the unwritten rules of your industry. Context is how you build shared reality with something that has zero access to your world.

The mistake most people make is either giving too little ("write a marketing email") or Drowning the AI with information that isn't structured in a way that helps it work efficiently.

What actually matters: The situation that created this need, who will use this output, what it needs to accomplish, and what constraints exist.

Notice the difference: audience specificity, constraints, timeline, and the unique angle that makes this campaign different from every other water bottle ad.

T - Task (The Work Breakdown)

"Make this better" isn't a task—it's a wish. AI needs verbs. Specific actions. A clear scope of work.

Here's how you get the best results: break complex work into numbered steps. This does two things. First, it gives AI a clear sequence to follow. Second, it makes it easier to evaluate what you got. If step 3 is weak, you can refine just that part without re-running everything.

Single-step task: "Analyze this email for clarity and suggest three specific improvements to make the main point more obvious."

Multi-step task: "Your task is to help me draft a creative brief by: 1) Identifying the 8-10 key components a creative brief should include, 2) For each component, providing a brief description of what it should cover, 3) Writing example content for each component tailored to the eco-friendly water bottle campaign, 4) Noting which components are most critical for this type of campaign."

One more thing: If your task has multiple valid approaches, tell AI which one you want. "Create three headline options" vs. "Create the single best headline" will give you very different outputs.

O - Output (The Format Specification)

This is where most people lose the most value. They get a wall of text when they wanted a table. A formal report when they needed casual bullet points.

Think about output in four dimensions:

• Format (numbered list, table, executive summary)

• Tone (conversational, formal, technical)

• Length (brief overview vs. comprehensive detail)

• Style elements (bold titles, examples for each point, specific formatting)

The less specific you are here, the more editing you'll do on the back end.

R - Review (The Quality Gate)

Here's something I learned scaling products at Hello Alice: the best way to reduce iteration cycles is to build the review step into the process itself.

Instead of generating output, reviewing it yourself, then asking for changes, you can have AI review its own work against your criteria before you see it. This typically catches 60-70% of the issues you'd otherwise catch manually—structural gaps, missing sections, inconsistent formatting.

What AI can catch: Completeness (did I include all requested components?), consistency (does the tone match throughout?), specificity (are examples generic or tailored?), and alignment with stated requirements.

What AI can't catch: Strategic soundness, whether the creative direction is compelling, if the positioning will resonate with your actual audience. That's still your job.

Putting It Together: The Full Prompt

Here's what the complete framework looks like in practice. Let's say you need to create a creative brief for an eco-friendly water bottle campaign:

• [Role] You are an experienced marketing strategist and copywriter.

• [Context] I need to develop a creative brief for a new advertising campaign promoting an eco-friendly water bottle. The campaign should appeal to environmentally conscious consumers and highlight the product's unique features.

• [Task] Your task is to help me draft a creative brief by identifying the key components that should be included. For each component, provide a brief description and example content tailored to the eco-friendly water bottle campaign.

• [Output] Please present the components in a numbered list format, with each component title in bold, followed by the description and example content in plain text. For example: Project Overview: Provide a summary of the campaign objectives and key messages. For instance, "We aim to increase brand awareness among eco-conscious consumers by highlighting the water bottle's sustainable materials and reusable design."

• [Review] After listing all the components, please review the creative brief for any missing elements and suggest enhancements to make it more effective.

Notice what's different from "just write me a creative brief": clear role assignment, specific context about audience and purpose, explicit task breakdown, defined output format, and built-in quality check.

What you get back is a structured creative brief with Project Overview, Target Audience, Key Messages, Unique Selling Proposition, Tone and Style, Deliverables, Timeline, Budget, and Measurements of Success—each with both description and tailored example content.

What You're Actually Building

Here's what happens when you get good at this: You start to see AI's limitations more clearly.

You realize it's excellent at structured content generation, information synthesis, and pattern-following. But it struggles with genuine creativity, nuanced judgment, and reading what's not said.

That clarity is valuable. It helps you use AI strategically—you handle the strategy, the judgment calls, the creative leaps. AI handles the structure, the synthesis, the pattern-based generation.

That's when it becomes a thinking partner instead of a frustrating tool that "never does what you want."

A Note on When to Use This

You don't need to write every prompt at this level of detail.

When I'm asking AI to summarize an article or explain a concept, I'll often just give it the task and output format. Quick requests don't need the full ACTOR treatment.

But for work that matters: prompts I'll reuse, outputs that go to clients, content that help me with my thinking. I make sure all five elements are there, even if I'm writing it on the fly. The framework isn't about being rigid. It's about being intentional with the elements that actually improve your results.

Think of ACTOR as a checklist, not a template. Role, Context, Task, Output, Review—if one of those is missing and your output is disappointing, you usually know exactly which element to add.

When you learn to speak AI's language, you're not dumbing down your requests—you're structuring them in ways that turn pattern recognition into strategic advantage.

Start with structure, not vagueness. Before you type anything into AI, write down: What role should it play? What context does it need? What specific tasks should it complete? What should the output look like? This five-minute planning step saves hours of frustration.

Test your prompts like you'd test a product. Run the same prompt multiple times. See where it's inconsistent. Refine the structure. Add more specificity where it's vague. Remove unnecessary complexity where it's over-engineered.

Build a prompt library for recurring tasks. When you create a prompt that works well, save it. Create templates for common scenarios: writing briefs, analyzing data, generating ideas, reviewing content. Over time, you'll have a collection of proven structures that amplify your capabilities.

"Am I asking AI to think like a human, or am I structuring my request to play to its strengths?" This reveals whether you're fighting against AI's nature or working with it. If you're frustrated with the output, the problem is usually in the prompt structure, not the AI's capability.

"Where does AI amplify my thinking, and where does it need me to provide judgment?" AI is a tool for augmentation, not replacement. The goal isn't to eliminate your involvement—it's to multiply your impact by handling the structured, pattern-based work while you focus on strategy, creativity, and judgment.

AI doesn't think like us, and that's not a limitation—it's a different kind of intelligence. The breakthrough comes when you stop expecting it to understand you like a human would, and start learning to communicate in ways that maximize its pattern-recognition strengths. Master this translation, and you unlock a thinking partner that amplifies your capabilities in ways you didn't think possible.

The shift to AI agents and autonomous systems is already happening. The professionals learning to communicate in structured requests now won't just have an advantage. They'll be fluent in the language of future work while others are still learning the alphabet.