Let’s set the scene: A Head of Product at a SaaS company, rushing before a board meeting, types a quick prompt into ChatGPT: “Give me a slide deck about our new feature launch.” The result is a generic, high-level spiel that could be about any product in any industry.
This is a classic case of casual user behavior. Fire and forget, hope for the best.
Meanwhile, a power user colleague tries a completely different approach. Before writing any prompt, she asks the LLM: “What kind of information do you need from me to create an effective slide deck for executives about a new product feature?” The AI agent responds with a structured list: audience details, feature specifics, business context, success metrics, competitive landscape, desired tone, etc.
Armed with this guidance, the power user can then provide a comprehensive prompt with all of those elements, resulting in a targeted, insightful draft that sounds like their company’s voice.
The difference was never in the AI’s capability, but in the questioning approach. Casual users tell the AI what to do. Power users ask the AI how to work together.
Why LLMs Mirror Your Questions
Large Language Models like GPT-4 don’t read your mind, they read your prompt. These models are giant statistical parrots: they’ll predict likely words based on your input and the patterns they’ve learned from billions of examples. LLMs follow explicit instructions more than any unspoken intent.
As one AI expert pointed out, you can think of an LLM as “a machine you are programming with words” [1]. If your instructions are ambiguous or incomplete, the model has no choice but to fill in the blanks, often with questionable guesses. Clarity, context, and constraints are the UX of prompting.
Here’s what most casual users miss: the best prompts often start with questions, not commands.
Instead of: “What do you know about our product launch?”
Try something like: “What specific information would help you write a compelling press release about our product launch? What questions should I be asking myself about our audience, message, and goals?”
The model will then be able to tell you what kind of information you’ll need to gather to make a compelling prompt that gets you much closer to what you want. Chances are, you’ll end up crafting a prompt together that looks something like this:
“You are a marketing expert. Our goal is to announce a new feature that cuts data processing time by 50%. Task: Draft a 3-paragraph press release for CIOs, in a factual tone, Constraints: under 300 words, mention our CEO’s quote from the briefing, Output: in Markdown format.”
Now you’ve worked with the model to specify a clear role, objective, facts, audience, tone, length limit, and format. Researchers have found that refining your query with specific context, explicit constraints, and clear goals “can significantly enhance the quality of results” [1].
Pro Tip: If you’re finding yourself writing a novel-length prompt chock-full of background info, it might be time to stop prompting and start curating a knowledge base. Past a certain point, adding more context in the prompt yields diminishing returns, especially if the info is very domain-specific or detailed. In enterprise settings, it’s especially smart to consider integrating a retrieval step.
3 Tips for Becoming an LLM Power User
The best fix for “bad output” on complex tasks is a two-step prompting workflow: 1) Meta-Prompting (ask the model what info it needs) and 2) utilize GICCO framework (Goal, Inputs, Constraints, Checklist, Output). If you’re doing a highly repeatable job, however, feel free to jump straight to tip #2.
Tip #1 → Mastering Meta-Prompting
Let’s walk through an example in which a seasoned Product Manager has to tackle a complex deliverable: writing a Product Requirements Document (PRD) for a new AI time series forecasting feature.
First, she starts with a meta-question:
“I’m a PM partnering with engineering and design to ship an AI time series forecasting feature. I need a production-ready PRD plus a go/no-go decision memo. What information should I gather and how should I structure my request to get an LLM output that won’t need major rewrites? Ask any necessary clarifying questions to get me as close to the ideal prompt as possible.”
Every LLM is different, but most AI agents will respond with something like:
A list of information to gather first (Problem statement, target user personas, success metrics)
LLM prompt structure
Clarifying questions for the PM to consider
The back-and-forth will continue until she has a clear picture of what information she’ll need to provide, and a template to write her consolidated, comprehensive prompt.
Meta-prompting can help prevent scope drift and can surface missing inputs before you waste time. However, one caveat to consider:
LLMs can hallucinate, and AI-generated content will always need human oversight and verification.
For hallucination mitigation, ensure that the LLM cites justifications for each of its recommendations with a quick sentence like “justify all your recommendations with reliable, cited sources.” If at some point you need to double-check that the LLM has conducted trustworthy research, the link to the source it used is often easy to find. Simply follow the URL & Ctrl/Cmd‑F whatever keyword pertains to the claim the LLM is making, and it’s easy to verify whether or not the LLM flubbed a fact or two.
By utilizing meta-prompting, you’ll end up with a far more tailored and accurate output.
Tip #2 → Utilize GICCO Framework
As mentioned, every LLM is different. Most of the time, it’s simply enough to use the meta-prompt to get you to a baseline you can begin editing. But sometimes, especially with higher-output models like GPT-o3, the meta-prompt can create an overwhelmingly long, complex prompt that tries to cover every possible angle.
That’s where GICCO comes in. GICCO (Goal, Inputs, Constraints, Checklist, Output) is your framework for when the AI gives you back a prompt that feels too sprawling or unfocused. It helps you distill the AI’s recommendations into a cleaner, more structured format:
Goal: What’s the single most important outcome?
Inputs: What key information does the AI actually need?
Constraints: What are the essential guardrails and requirements?
Checklist: What are the must-have success criteria?
Output: What specific format and structure do you need?
Think of GICCO as an editing tool, a way to take the AI’s comprehensive prompt and streamline it for maximum clarity and focus. You’re simply refining what the AI already gave you.
That being said, if you already have a well-defined goal, key information, constraints, and success criteria, you can absolutely skip the meta-prompting entirely and jump straight into GICCO. This works especially well for repeated tasks. Creating a monthly report in a familiar format? GICCO might be faster than the collaborative discovery process. Drafting a monthly newsletter? You probably know exactly what you need.
Most power users still prefer meta-prompting to merely GICCO, as the AI often surfaces considerations the user didn’t think of. Even when you know exactly what you want, that initial questioning session can reveal blind spots that may not have been considered.
Tip #3 → Ask Advanced Questions
It’s becoming more widely known that AI models have a subtle confirmation bias. They tend to agree with your framing and support your initial direction, rather than challenging what you’ve said. Bias mitigation is a key part to being an LLM power user. Phrase your question strategically to force the AI out of blind agreement mode.
Challenge Your Assumptions
If you settle on a leading question like “Is this approach good?” the AI will likely find reasons to support the approach. Instead, you need to explicitly demand opposition, and use your own discretion to see if there’s solid reasoning behind it.
Instead of: “What do you think about this marketing strategy?”
Try something like:
What’s the strongest counterargument to this marketing approach?
What would our biggest competitor say is wrong with this strategy?
If I were presenting this to a board of skeptics, what would be their top 3 objections?
Iterate for Excellence
Most people stop at “good enough,” and AI will happily validate that stopping point if you let it. But AI doesn’t have ego or fatigue, it can push your thinking beyond your natural comfort zone and suggest improvements you may not have considered. The key is asking questions that assume your current solution isn’t the ceiling.
Instead of: “Is this solution adequate?”
Try something like:
Assume this current approach will only get us 70% of the way there. What would the remaining 30% look like?
If our biggest competitor saw this solution, how would they make it 10x better with unlimited resources?
What would this look like if we optimized purely for [specific constraint like speed/cost/user experience]?
Copy-and-Paste Prompt Starters
We’ve already equipped you with the Meta-Prompt and an example GICCO, but here are some complementary prompt examples to help you get started:
The Strategic Interviewer
Good for: Complex projects where the scope isn’t yet clear.
I’m a [role]. Goal: [artifact + purpose]. Audience: [who]. Constraints: [key limits].Ask me the top 10 clarifying questions you need to produce an excellent result.The Assumption Challenger
Good for: Stress-testing ideas and strategies.
Using the context above, propose a numbered, step-by-step plan.For each step: purpose, acceptance criteria, 1 risk, and a simple success metric.Wait for confirmation before executing any step.The Perspective Multiplier
Good for: Getting multiple viewpoints on complex decisions.
Self-evaluate the output against this rubric: [list 5–7 criteria].Return a Pass/Fail table with 1–2 fixes per Fail. Apply fixes and reprint the final.Pro tip: If you have time, include a copy-and-paste “system preface” (tone rules, banned phrases, brand terminology). Consistency in → consistency out.
When “Better Prompting” Isn’t Enough
Generic LLMs trained on public internet data will always have gaps in your specific domain. When models encounter knowledge gaps, they’ll try to fill them by "inventing probable phrases", which is something you can avoid if you invest in knowledge base curation.
If the task depends on proprietary, fresh, or niche information, you need to stop cramming context into the prompt and wire the model to your data:
Curate a knowledge base of policies, specs, decks, FAQs, cases.
Index it for retrieval so the model can pull only what’s relevant.
In‑prompt guardrails: “Use only the retrieved snippets; always cite the source ID.”
This retrieval-augmented approach is how you cut hallucinations and make recommendations explainable. Our knowledge curation services help organizations build these domain-specific knowledge pipelines, transforming scattered information into LLM-ready formats that dramatically improve accuracy. For customer-facing applications, consider our Virtual AI Concierge solution, which combines intelligent prompting with curated knowledge bases to create reliable, helpful AI assistants that actually know your business.
From Casual Prompter to Power User
The gap between casual and power users has never been about technical knowledge. It’s all about approach. Here’s a casual user’s quick path to success:
Start every complex request by asking the AI what it needs to succeed. Use the two-pass method: meta-question first, then structured prompt. Challenge your own assumptions by asking for counterarguments and alternative angles. And most importantly, treat the AI like a thinking partner rather than a magic content machine.
The transformation to power user happens fast once you know where to start.
Ready to move beyond basic prompting?
Explore our other custom AI solutions to see how different AI approaches can accelerate your innovation.
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Sources:
MIT Sloan Teaching & Learning Technologies. (n.d.). Effective prompts for AI: The essentials. Retrieved July 30, 2025, from https://mitsloanedtech.mit.edu/ai/basics/effective-prompts/
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