AI has become a core tool in the modern workplace, and the coworking industry is no exception. As more operators adopt tools like ChatGPT, Claude, Gemini, to streamline tasks, improve member experience, and enhance decision-making, one skill rises above the rest: prompting.

Prompting isn’t just about asking a question, it’s about guiding an AI model to deliver precise, high-value outcomes. Below is a practical guide, built from real examples, frameworks, and best practices, to help coworking teams elevate how they interact with AI.

As prompting becomes the new essential skill, everyone is now interacting with LLMs to create meaningful conversations with AI models. But the real question is: how can we do it better?

Here’s how the industry is approaching it, and what we’ve learned along the way.

Let’s begin with OpenAI’s three-step framework for effective prompting:

OpenAI recommends using this structure:

In essence: better input → better output.

Let’s go through the three steps with two examples.

Basic prompt:
“Can you help me understand the current trends in global markets?”

To improve the outcome, include context, role, and expectation in your prompt.

Add Context + Role:
“I am a business analyst at Acme Co exploring global market trends. Can you help me understand the key trends across industries and regions?”

Add Expectations:
“I am a business analyst at Acme Co exploring global market trends. Can you help me understand the major trends impacting global markets, focusing specifically on how technological advancements and geopolitical shifts are influencing various industries? Please highlight the key trends, assess their potential risks, and identify challenges organizations might face in adapting their strategies.”

Let’s look at another example.

Basic prompt:
“Can you help me analyze the attached product feedback?  [upload document into chat]”

Add Context + Role:
“I’m a product manager at Acme Co, analyzing feedback on our latest product release. Can you help me identify common themes in the attached user feedback?”

Add Expectations:
“I’m a product manager at Acme Co focused on improving our latest product release. Can you analyze recent user feedback to identify key themes and prioritize areas for improvement? Please highlight any recurring issues, assess the potential impact on user satisfaction, and suggest actionable steps we could take to address high-priority concerts”

These three steps help us improve the quality of outcomes.

As a coworking operations manager using ChatGPT, here are versatile prompts tailored to your daily workflow using OpenAI’s three-step prompting framework.

Let’s walk through the three steps using four examples.

“I manage operations for a coworking space and have gathered detailed qualitative feedback from members about their experiences, including amenities, community, pricing, and daily operations. You are a coworking operations and customer experience expert with deep knowledge of member satisfaction and retention strategies. Analyze the following member feedback to identify the most frequent and meaningful recurring themes, clearly distinguishing positive patterns from key pain points. Then recommend three concrete, actionable improvements that are realistic for a coworking operator to implement and most likely to improve member satisfaction and retention. For each recommendation, briefly explain the expected impact and why it addresses the identified issues.”

Member feedback:
‘’’[insert feedback]’’’”

“I manage operations for a coworking space and regularly review weekly meeting room booking data to improve space utilization and member experience. You are a coworking operations and space-optimization expert with experience in data-driven facility planning. Analyze the provided weekly room booking data to identify peak usage periods, underused rooms or time slots, and inefficiencies in current allocation. Then present clear, actionable insights in bullet points, including specific opportunities to optimize meeting room availability, scheduling policies, or room configurations to better meet member demand and maximize utilization.”

Room booking data:
‘’’[insert data]’’’”

“I oversee community engagement for a coworking organization and am looking to strengthen relationships among members across different teams and locations while improving long-term retention. You are a community-building and coworking engagement strategist with experience designing scalable, high-impact programs. Propose five monthly initiatives or events that would meaningfully strengthen member relationships, encourage cross-team collaboration, and support retention at our coworking spaces. For each initiative, briefly describe the concept, the target audience, and the primary retention or collaboration benefit. Keep the ideas practical, repeatable, and appropriate for ongoing monthly execution.”

“I manage operations for a coworking space and am responding to a member concern that may impact satisfaction and retention. You are a customer support and coworking operations communication expert known for empathetic, professional conflict resolution. Based on the issue described below, draft a clear, empathetic, and member-first response that acknowledges the concern and reinforces our commitment to a positive experience. Then outline two realistic solution options, explaining how each addresses the issue while balancing member needs and operational feasibility.”

Member issue:
‘’’[insert member issue]’’’”

This is the framework OpenAI proposes for better prompting. Now, let’s look at how Gemini approaches it.

The Gemini Four-Step Framework is a structured prompting approach designed to improve the relevance of AI generated responses. By clearly defining who the AI should be, what it needs to do, the situation it operates in, and how the output should be delivered, this framework helps reduce ambiguity and produces higher-quality outcomes.

Gemini recommends using this structure:

Let’s go through the four steps with two examples.

Prompt 1:

“I am an executive administrator to a team director. Our newly formed team now consists of content marketers, digital marketers, and product marketers. We are gathering for the first time at a three-day offsite in Washington, DC. Plan activities for each day that include team bonding activities and time for deeper strategic work. Create a sample agenda for me.”

Prompt 2:

“Suggest three different icebreaker activities that encourage people to learn about their teammates’ preferred working styles, strengths, and goals. Make sure the icebreaker ideas are engaging and can be completed by a group of 25 people in 30 minutes or less.” 

Prompt 3:

“Organize this agenda in a table format. Include one of your suggested icebreakers for each day.” 

“Help me craft an empathetic email response. I am a customer service representative, and I need to create a response to a customer complaint. The customer ordered a pair of headphones that arrived damaged. They’ve already contacted us via email and provided pictures of the damage. I’ve offered a replacement, but they’re requesting an expedited shipping option that isn’t typically included with their order. Include a paragraph that acknowledges their frustration and three bullet points with potential resolutions.”

And finally, a few concepts worth mentioning:

Your prompts should:

Without a clear role or perspective, the AI defaults to generic or high-level answers.

Delimiters separate text and help avoid confusion, great for long inputs.

Example structure:

You are given text delimited by triple quotes.

Step 1 - Read the text

Step 2 - Provide feedback on grammar and structure

Step 3 - Rewrite the text with recommended edits

Step 4 - Translate the text to French and to Spanish

”””

Text

”””

To conclude, here are some key guidelines worth keeping in mind:

Prompt engineering is about learning how to communicate more effectively with AI systems. For coworking professionals, the ability to provide context, role, set expectations, and guide tone can turn AI from a basic assistant into a relevant operational partner.

Whether you’re managing spaces, supporting members, or shaping strategy, better prompts lead to better outcomes.

And we’d like to share one final idea that we explored quite a lot:

One of the most overlooked aspects of prompting is that the quality of the output is limited by the quality of the questions we think to ask in the first place.

When we interact with AI, we often jump straight to solutions:

But great prompting, and great decision-making, starts one step earlier.

Often, what we’re missing isn’t more data or better answers. It’s clarity about what we should be exploring.

Strong prompts don’t just return answers; they help surface unknown unknowns. They push us to challenge assumptions, reveal blind spots, and uncover questions we didn’t realize needed to be asked.

Instead of immediately prompting an AI to optimize or refine something, an alternative approach is to first use prompting to generate deeper, exploratory questions.

Here’s an example of how prompting can guide that exploration:

“I’m evaluating how to optimize a coworking space. Before proposing solutions, help me identify the most important questions I should ask myself across space usage, member experience, operations, and business goals. Focus on questions that reveal blind spots, assumptions, and trade-offs I may not be considering.”

This prompt might surface questions such as:

This is the real power of prompting.

AI isn’t just a faster way to get answers, it’s a thinking partner that can help you:

By using prompts to explore what to ask, not just what to do, coworking operators can achieve more relevant outcomes and avoid premature optimization.