---
title: AI Is Only as Good as the Working Relationship You Build With It
description: Discover how effective collaboration with AI transformed our website rebuild, emphasizing the importance of communication and context in maximizing AI's potential.
image: https://limitlessconsulting.agency/hubfs/Limitless%20Field%20Notes%20-%20AI%20Usability-1.png
---

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 October 14, 2026

# AI Is Only as Good as the Working Relationship You Build With It

![Picture of Brooke Hamrick](https://limitlessconsulting.agency/hs-fs/hubfs/Brooke%20H%20Limitless.png?width=50&name=Brooke%20H%20Limitless.png) [Brooke Hamrick](https://limitlessconsulting.agency/blog/author/brooke-hamrick)

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## How we used AI to rebuild our website, what we learned along the way, and why the way you work with AI matters as much as the tools themselves.

I've spent a good portion of my career helping companies adopt new technologies, improve processes, and get more value out of the tools they're already paying for.

And if there's one thing I've learned, it's that technology rarely solves a problem simply because you've implemented it.

AI is no exception.

There's no shortage of content telling us how AI is going to transform the way we work. There are endless lists of prompts, tools, use cases, and productivity hacks. Some of it is genuinely useful. A lot of it makes AI sound like a vending machine where you insert the right combination of words and out comes a perfect deliverable.

That's never been how I've approached it.

I use AI every day, across nearly every aspect of running Limitless Growth Consulting Agency. And over time, I've found that the greatest value comes from treating it much like I would a coworker.

I give it context. I will explain what I'm trying to accomplish. I challenge its thinking. I ask follow-up questions. And when something isn't right, I tell it why.

Recently, Shar and I put that approach to work on a project that's particularly close to home: rebuilding our own website.

And it turned out to be a pretty good example of what AI collaboration can look like in practice.

## **A Website Rebuild, With a Little Help From AI**

Our website had reached a point where it wasn't doing a particularly good job of representing who we are or the work we do today. Like a lot of growing businesses, our services had evolved, our experience had expanded, and the website hadn't quite kept up.

We wanted to rethink how we presented our services, improve navigation, strengthen our search visibility, showcase our client work, and tell our story in a way that actually sounded like us. We also wanted to do most of the work ourselves.

That's where AI came in.

We used ChatGPT, Claude, and Codex throughout the project, but in several different capacities.

## **First, as a thought partner.**

Before we started making changes, we spent time discussing what the website needed to accomplish.

We evaluated how our services should be organized, how prospective clients might look for the problems we solve, and how to make the content more accessible to both traditional search engines and AI-powered search.

We worked through questions about page structure, search engine optimization (SEO), answer engine optimization (AEO), navigation, calls to action, and how to present our experience without overwhelming visitors.

Some conversations were strategic while others were incredibly tactical. And plenty involved me saying some variation of, "Nope, I don't like that for x reason. Let's try again."

## **Next, as a collaborator.**

Once we had a direction, we started working through the actual content. We rewrote service descriptions, developed FAQs, refined case studies, updated page titles and meta descriptions, and spent a considerable amount of time working on our About Us page.

That last one was especially close to my heart.

Shar and I have a long history together, and we wanted the page to reflect our actual relationship, our different strengths, and the experiences that led us to build Limitless. It took several rounds of iterating to get there.

I shared details about our personalities, how we approach problems, where our strengths overlap, and how we complement each other. I shared the things we giggle about when we’re working long hours, and how we disconnect and refresh.

When the writing felt too corporate, I said so. When the perspective shifted awkwardly, I pointed it out. When something felt generic, we kept working.

Eventually, we landed on something that *genuinely* felt like [our story](https://docs.google.com/document/u/2/d/19TWQIAmNLxElWvQGER8L8HVEp6OwAZoduGBFGyFlwJE/edit).

## **Then, as an execution resource.**

This is where things got particularly interesting.

While I was using ChatGPT to develop strategy, troubleshoot HubSpot, and work through decisions, we were using Claude to uplevel and polish existing content. And Shar was using Codex functionality to make the actual website updates faster, like actually building out portions of our case study pages while she watched and guided.

We had AI helping us plan the work, collaborate on the content, and, in some cases, carry out the changes. And we still maintained ownership of the decisions and final output.

## **Finally, as a second set of eyes.**

Throughout the project, I brought pages back to ChatGPT for review.

We looked at content consistency, SEO considerations, formatting, page structure, and whether the finished pages aligned with what we'd originally set out to accomplish.

Was every review perfect? Definitely not. There were times when ChatGPT couldn't reliably access a page, misunderstood what I was asking, or made recommendations that didn't fit our situation.

And there were definitely moments when I had to push back. But that was part of the process, too.

The value came from having another resource available to help us think through the work, catch potential issues, and keep moving.

## **The Biggest Difference Is How You Communicate**

One thing I've noticed when talking with other people about AI is how dramatically our experiences can differ.

Someone will tell me they tried one of the various AI tools and weren't impressed with the results. When I dig in and ask questions, I usually end up at one of two conclusions.

1. The prompt is lacking detail, guidelines, examples, and the essence of what they’re actually trying to do.
2. They don’t spend any time training their tools to actually get better over time.

An example of number one might look something like:

"Write a blog about sales leadership." That's it.

Now imagine walking up to a new employee, giving them that instruction, and expecting something insightful, relevant, and written in your voice. You'd probably be disappointed.

I approach AI much the same way I approach delegating work to a person. I explain the objective, the audience, the context, the constraints, and what a successful outcome should look like.

If I'm asking for a piece of content, I might share examples of previous writing, explain the tone I'm after, identify specific points I want covered, and call out language I want to avoid.

If we're solving a business problem, I'll explain the systems involved, the operational realities, the available resources, and the tradeoffs I'm willing to make.

Sometimes my prompts are several paragraphs long. But that’s why I get the results I want in less time. It’s the old “Give me six hours to chop down a tree and I will spend the first four sharpening the axe” saying.

The more clearly I can communicate what I'm trying to accomplish, the better the starting point tends to be.

Of course, you don't need to know every detail upfront. Some of my best conversations with AI start with a half-formed idea. But as we work through it, I continue adding context and refining the direction.

Just like I would with a colleague.

## **I Give AI My Edits Back**

Let’s lean into number two. This might be one of the *most useful* habits I've developed, and most people I chat with aren’t doing it.

When AI helps me write something, I rarely use the first version exactly as written. If the tone is completely wrong or it’s a miss, I usually continue to prompt in increasing detail until I am happy with the draft.

But it’s still just a draft. I make changes. Sometimes they're minor. Other times I end up rewriting entire sections.

But when I'm finished, I often share my edited version back with my AI tool.

I'll explain what I changed and why. Maybe the original sounded too formal. Maybe it relied on phrases I'd never actually use. Maybe I wanted the message to feel more direct, more conversational, or more grounded in practical experience.

I want the AI to have examples of what good looks like to me. Because over time, that feedback can improve the context available for future work, particularly when preferences are retained through memory or project instructions.

It doesn't mean the system automatically learns every edit permanently. But providing examples and explaining your decisions gives it a much better chance of producing something useful the next time.

There's also a benefit for me. The process forces me to articulate what I actually like and dislike about my own writing.

And that clarity makes me a better communicator, regardless of whether I'm working with AI or another person.

## **Don't Be Afraid to Tell It It's Wrong**

Here's another thing I think people underestimate.

AI can be incredibly convincing, even when it's incorrect.

It can confidently recommend a feature that doesn't exist, misunderstand a technical limitation, or make assumptions that don't hold up in your particular environment. I've experienced all three.

Especially when we're working through complex Salesforce or HubSpot configurations.

My response is usually to ask more questions. Why are you recommending that approach? Is there a simpler option? What are the implications for our existing data model? Are you certain that functionality exists?

Sometimes I already know the answer and want to challenge the recommendation. Other times I'm trying to understand something unfamiliar.

Either way, I don't treat the first response as the final answer. And I don't think anyone should.

One of the most valuable things AI can do is help you explore a problem from multiple angles. But you still need to apply judgment, verify important information, and recognize when the tool has reached the limits of what it can reliably do.

That matters even more when you're using AI in client environments where security, data integrity, or business decisions are involved.

## **Know When to Collaborate and When to Delegate**

Our website project also reinforced something I think will become increasingly important as AI tools evolve. There are different levels of AI involvement, and they don't all serve the same purpose.

Sometimes I want a sounding board.

Sometimes I want someone to challenge my thinking.

Sometimes I need help researching options or troubleshooting a technical problem.

And sometimes I have a clearly defined task that I'd like AI to execute. Those are different needs.

For example, when we were deciding how to structure our services page, I wanted to be involved in the thinking.

We discussed the categories, considered how visitors would interpret them, and refined the language together. But once we'd agreed on a structure for our case studies, there were portions of the implementation that made sense to delegate through Codex.

The important part was knowing which decisions required our involvement and which tasks could be carried out with clear instructions and appropriate review. That distinction applies well beyond website development.

It's relevant to reporting, research, sales enablement, process documentation, CRM administration, and countless other day-to-day activities.

## **You Still Have to Know What Good Looks Like**

I think this is where experience becomes especially valuable.

AI can produce a polished-looking deliverable very quickly. But polished doesn't necessarily mean accurate, useful, or appropriate for the situation. You need enough understanding of the work to evaluate the output.

When we're reviewing a sales process, I can recognize recommendations that sound reasonable on paper but would create unnecessary work for the team.

When we're evaluating a CRM configuration, I know to consider the downstream impact on reporting, automation, integrations, and user adoption.

And when we're writing about Limitless, Shar and I know whether the content accurately reflects our experience and values. AI helps us move through the work faster, but our experience is what allows us to assess the results.

For people who are earlier in their careers, this is worth paying attention to. AI can be an incredible learning resource. Ask it to explain its reasoning, compare approaches, identify risks, and help you understand unfamiliar concepts.

Just don't let the convenience of getting an answer replace the process of developing your own judgment.

## **The Real Productivity Gain Isn't Always About Speed**

When people talk about AI productivity, the conversation usually centers on time saved. And yes, there are plenty of tasks that take significantly less time with AI. But I think that misses part of the value.

For me, AI has made it easier to explore ideas that might otherwise stay on the back burner. It's made it easier to start projects that feel overwhelming. It's given me a resource to work through decisions when I don't have another person available to bounce ideas off. And it's helped Shar and I tackle work that might otherwise have required additional outside resources.

Our website is a great example.

We didn't simply ask AI to build us a website. We used it to help us make dozens of decisions, work through challenges, create and refine content, execute portions of the project, and evaluate the results.

Along the way, we learned more about our own website, our messaging, and the tools we were using. And that knowledge will stay with us. That's a pretty meaningful return on the time invested.

## **A Few Things I'd Encourage You to Try**

If you're looking to get more value out of AI in your everyday work, here are a few habits worth developing:

**1. Give it the context you'd give a coworker.** Explain the problem, the objective, the audience, and any important constraints. Don't assume it knows what you know.

**2. Have a conversation.** Ask follow-up questions. Explore alternatives. Challenge recommendations. You don't have to get everything into one perfect prompt.

**3. Share examples of your work.** Whether it's a writing sample, a report, or a process document, examples help establish your expectations.

**4. Give feedback on the output.** When you make changes, explain them. Show it what you preferred and why.

**5. Ask it to challenge you.** Sometimes the most useful response is the one that identifies something you hadn't considered.

**6. Match the tool to the task.** Use conversational AI for collaboration and reasoning. Consider more autonomous tools for well-defined execution tasks, with appropriate access controls and review.

**7. Stay accountable for the result.** Verify facts, test technical recommendations, and review anything that will affect customers, employees, or business operations.

And perhaps most importantly, don't feel like you need to become an expert in prompt engineering before you can get started.

If you know how to clearly communicate expectations, provide useful feedback, and collaborate with other people, you already have a lot of the skills you need.

## **One Last Thought**

I've worked with some incredible people throughout my career. The best working relationships have always involved open communication, mutual understanding, constructive disagreement, and a willingness to keep improving.

Those are also the principles I bring to working with AI.

Obviously, AI isn't a person. It doesn't have the same understanding, accountability, or judgment that a human colleague brings to the table. But approaching the interaction with the same care and clarity I would give a coworker has made a meaningful difference in the value I get from it.

Our website rebuild was a great reminder of that.

And if you're curious what came out of all those conversations, revisions, occasional disagreements, and a few moments of genuine excitement, you can see the results at[limitlessconsulting.agency](https://limitlessconsulting.agency/).

We're pretty proud of how it's coming together. And yes, AI helped us write parts of this article, too.

[ai](https://limitlessconsulting.agency/blog/tag/ai), [chatgpt](https://limitlessconsulting.agency/blog/tag/chatgpt), [codex](https://limitlessconsulting.agency/blog/tag/codex), [revops](https://limitlessconsulting.agency/blog/tag/revops), [efficiency](https://limitlessconsulting.agency/blog/tag/efficiency), [productivity](https://limitlessconsulting.agency/blog/tag/productivity), [claude](https://limitlessconsulting.agency/blog/tag/claude)

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