Breaking Barriers in AI
The Power—and Limitations—of an Enterprise LLM
Artificial Intelligence has long been heralded as the ultimate productivity booster, but harnessing its full potential requires skill, practice, and a deep understanding of prompt engineering. At my workplace, I was fortunate to gain access to a hugely powerful Large Language Model (LLM) designed specifically for enterprise use. One of its standout features was its ability to let users create prompts by defining variables by letting users create and share a prompt template, making it easier to generate dynamic, efficient responses without extensive prompt-writing expertise. Of course, I had to give it a cool name and acronym. I named it the Variable Prompting Method (VPM).
For users unfamiliar with AI, this feature was a game-changer. By simply inserting replacing the variables in the template with their own information, they could create structured, meaningful prompts without the trial-and-error learning curve that often accompanies AI interactions. This should have made prompt creation seamless, bridging the gap between AI novices and expert users. However, in practice, things were far from perfect. The system, though powerful, has a steep learning curve that many users would find intimidating. The rigid template structure limited flexibility, and for those who wanted more customization, it became a frustrating experience. It was clear that while VPM had potential, its implementation was holding it back from being truly useful.
The Frustrations of an Incomplete System
As I explored the VPM functionality, I quickly ran into a host of implementation issues. The LLM functionality, though impressive, was still a work in progress. I found myself trapped in a paradox: the tool meant to simplify AI engagement still required an advanced understanding of how prompts worked in order to troubleshoot its own limitations.
The primary issue was rigidity. The structured variable system left little room for nuanced prompts, which are often necessary in real-world applications. Users like me who tried to push the boundaries of customization found themselves rewriting prompts repeatedly, sometimes spending more time troubleshooting than actually using the tool. For users with no prior experience in prompt engineering, the learning curve would be especially steep, making AI adoption slower than anticipated. Even as an experienced AI user and prompt engineer, I found myself frustrated. I wanted to take full advantage of variable prompting, but I was constantly running into roadblocks—sometimes due to the model’s own constraints, other times due to the rigid implementation of VPM itself.
It became clear that if I wanted a solution, I had to create it myself.
Turning to AI for an AI Solution
Faced with this challenge, I did what any modern-day problem-solver would do: I turned to the LLM itself for advice. Ironically, while the VPM feature had limitations, the LLM was still incredibly useful as an idea generator. I prompted it to suggest alternative ways to implement a simpler, more accessible method that would provide the benefits of variable prompting—without requiring users to struggle with the built-in system’s quirks. What did the AI's responses tell me? Think outside the system. Instead of trying to fix the enterprise LLM’s variable prompting structure, I could design an external tool—something that would allow users to define their variables in a more intuitive way, then generate a complete prompt they could copy and paste into the LLM manually. This was an epiphany. Instead of being bound by the rigid, incomplete nature of the system, I could leverage AI to work around its own limitations. The idea was to use AI guidance and create a separate tool or a space where users could craft their prompts with ease and then bring those structured prompts into the LLM without the constraints of the built-in VPM.
Innovating with a Low-Tech, High-Impact Solution
With that insight, I turned to the most advanced tool ever designed by humans — Microsoft Excel. Yes, forget neural networks and deep learning; Excel has been the silent, omnipotent genius running organizations since the dawn of spreadsheets. If AI were a sentient being, it would probably call Excel its wise, battle-hardened ancestor.
Using Excel, I built a simple yet highly effective solution:
Users could input key variables into a structured table, eliminating the need for them to manually adjust prompts every time they wanted variations.
The sheet would dynamically generate a fully customized prompt using these inputs, ensuring accuracy and coherence.
The completed prompt could then be copied and pasted into the LLM, allowing for instant usability and seamless integration with the existing AI system.
This low-tech but high-impact solution meant that anyone—regardless of their AI experience—could harness the power of structured prompting without getting tangled in the LLM’s built-in limitations. It was intuitive, easy to use, and eliminated the need for complex AI knowledge or programming expertise. The real beauty of this approach is its accessibility. Excel remains a tool that nearly everyone in the workplace is already familiar with, removing the intimidation factor that often accompanies AI adoption. Instead of struggling with predefined formats, users can now customize prompts effortlessly, opening the door to greater efficiency and wider AI adoption within the organization. Moreover, the tool is designed to grow organically.
As users become more comfortable with structured prompting, they will be able to add more details into each table cell, guiding the spreadsheet to accommodate their specific needs. Over time, this will lead to an evolving, self-sustaining system where teams can share templates, refine best practices, and optimize their AI interactions without additional technical support. Additionally, this method allows for scalability. By embedding structured prompt generation into a universally accessible tool, the organization ensures that AI usage isn’t limited to a select few but is instead democratized across multiple departments. This is no longer a niche AI feature—it is an enterprise-wide enabler of efficiency. Beyond that, it fosters a culture of AI innovation. Employees, once hesitant to engage with the LLM now have a structured way to use it effectively. Instead of fearing the complexity of AI, they will see it as a tool that works for them, not against them. This shift in perception is key to unlocking higher adoption rates and more effective AI-driven workflows.
Democratizing AI in the Workplace
This experience taught me a valuable lesson: innovation doesn’t always require advanced technology—sometimes, it just takes a shift in perspective. The most effective solutions are often the simplest ones, and true innovation comes from adapting existing tools to meet evolving needs.
By bypassing the constraints of the LLM’s variable prompting system and leveraging a simple spreadsheet tool, I was able to provide a practical, immediate, and scalable solution to a pressing problem. More importantly, this approach will democratize LLM usage across my workplace, empowering more users to engage with AI confidently and effectively. No longer was AI prompt engineering a mysterious and exclusive skill set—suddenly it has become a shared organizational asset, accessible to all. This journey was a testament to the power of human ingenuity and adaptability—a reminder that sometimes, the best way to solve a problem with AI is to think beyond AI itself. It underscored the importance of rethinking AI integration, shifting from rigid systems to flexible, user-driven solutions that complement technology rather than constrain it.
Looking forward, I see even greater possibilities. With more refinement, this external prompting system could evolve into a fully automated solution, integrating APIs for seamless input and output, or even be transformed into a lightweight app for an even smoother user experience. The fundamental takeaway, however, remains unchanged: AI isn’t just for tech experts—it’s for everyone, and it’s time we design our systems to reflect that reality.
