Giving your team access to an LLM is not an AI strategy. It is a liability shift. If your employees are prompting from scratch every day, they are not executing a system. They are inventing one. And most of them are inventing a highly inefficient one.

The real issue with enterprise AI is not adoption. It is the token drain. Most professionals waste eighty percent of their AI credits on repetitive, trial-and-error follow-up prompts. This occurs because their initial prompt was too vague, forcing the system to guess the context, the target audience, and the output requirements. Treating an advanced large language model like a basic search engine is an operational leak. When you ask a system to "write a report" without defining the role, constraints, or evidence requirements, you guarantee a generic draft that requires extensive manual rewriting. You are paying for technology only to spend human capital fixing its output.

This is where operational discipline matters. I learned the value of operational discipline in environments where slow, unvetted, or inaccurate execution had immediate consequences. As a disabled U.S. Air Force veteran and former SERE (Survival, Evasion, Resistance, and Escape) instructor, I spent years teaching operators how to execute under extreme pressure using strict, repeatable standards. Later, as a civilian consultant, I applied those same systems-engineering principles to disaster response logistics in Haiti after the 2010 earthquake. In an environment with no supply chain, no infrastructure, and zero margin for error, you quickly learn that data is not a luxury — it is the system that decides who gets supplies and when. That operational mindset is exactly how I approach technology. Whether I am building API-based databases that eliminate thirty hours of manual reporting labor per week for e-commerce retailers, or helping a mid-market company select an AI vendor, the core challenge is identical: you must define the system before you scale the execution.

If your organization does not have a standard for how prompts are written, you are leaking capital. The first step in prompt discipline is Task-Type Classification. Before writing a single word, the operator must classify the task. Is it research, technical writing, business strategy, data analysis, or automation? Each task type requires entirely different guardrails. A research prompt must enforce strict citation standards and uncertainty handling. A business strategy prompt must define financial constraints and risk tolerance. A data analysis prompt must specify the statistical methods and data cleaning rules. When you force this classification early, you eliminate eighty percent of AI drift and hallucinations. You move from hopeful prompting to deterministic execution.

I built a custom tool to automate this discipline for my clients. It is called the Prompt Mentor. It stops vague execution by acting as a diagnostic gate. It evaluates the user's input, identifies what is missing, and asks the exact, high-leverage clarifying questions needed to turn a weak prompt into a production-ready specification before a single token is wasted.

You can claim this skill for free and start running it in your workflow today.

👉 Claim the Free Prompt Mentor Skill: https://skillrefinery.ai/@jehconsulting/claim/prompt-mentor

If your organization has not reviewed its AI prompting standards recently, it may be worth a structured conversation.

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Jason Hersh

JEH Consulting Services