Why Your Best AI Prompt Is Your Own Work

Stop Explaining the Problem When You Can Show It

There is a profound difference between asking AI for general advice and giving the system something real to work with. This distinction is becoming increasingly important in every professional field. OpenAI recently shared examples of how experts, ranging from professional race car drivers to demolition planners, use ChatGPT in their daily tasks. What stands out is not any single profession, but the common pattern behind their usage. These users are not just asking generic questions. They are embedding ChatGPT directly into the work itself.

Consider the example of demolition professional James Costello. Instead of asking how to estimate a demolition job, he provided his specific architectural and civil removal plans to the AI. He asked for an analysis of the scope and a calculation of material quantities based on that unique documentation. The second approach provides the AI with actual evidence, while the first only triggers general, perhaps less relevant, advice. This principle applies across every industry. Developers can provide error messages, source code, and screenshots. Business owners can share sales data and specific customer feedback. Consultants can offer reports and raw requirements. Stop describing your challenges manually, and start providing the actual material you are already working with.

Context Is More Important Than Clever Prompting

For a long time, the spotlight has been on prompt engineering, or the attempt to find magical combinations of words to unlock superior AI responses. I believe the more valuable skill is actually context engineering. The goal is not to find a perfect phrasing, but to provide the information necessary to solve the problem correctly. A useful structure for any interaction includes: Context, Evidence, Desired Outcome, Constraints, Task, and Verification.

Instead of simply saying, "Fix this feature," you could provide the existing implementation, a screenshot, and the error. Then, explain what the feature is supposed to do, what it currently does, what must not change, what you have already tried, and how you will confirm the fix is successful. When you follow this path, the AI is no longer guessing what your situation looks like. It is reasoning from concrete evidence.

AI Becomes Valuable When It Enters the Workflow

Professional race car driver Sara Choi provides a perfect example of this in action. She does not just ask about driving technique. She supplies a very specific context, including the vehicle, current track conditions, and the specific technique she is trying to master. This represents a significant shift. The first generation of AI usage was largely a question and answer model. A much more powerful model involves a loop of evidence, analysis, action, and verification. You give the AI the real situation. It performs an analysis. You take an action, and you bring the results back to the system to complete the loop.

The Failure Is Your Best Asset

When you are developing software or analyzing data, the instinct after a failure is often to rewrite the prompt and try again from scratch. This is frequently the wrong move. The failure itself is a valuable piece of data. Instead of restarting, show the AI exactly what went wrong. Provide the error message, the unexpected output, the logs, or the test results. Ask the system, "What does this new evidence tell us?" This changes your development process from repeated guessing into iterative troubleshooting. The process becomes: Attempt, Observe, Diagnose, Correct, and Verify. Each step should reduce the total amount of uncertainty in your project.

Files Are the New Prompt

A prompt is no longer just text typed into a box. It can and should include PDFs, spreadsheets, screenshots, source code, images, emails, and research papers. The artifact itself is the context. As we move forward, the most effective AI users will be those who know exactly what information to provide rather than those who are simply good at asking questions. If the evidence exists in your files, give it to the AI. Do not spend time describing a screenshot you can upload. Do not explain a spreadsheet row by row if the file can be uploaded and analyzed directly. Give the AI the source material first, and then ask it to reason. The result will be vastly more accurate and applicable to your needs.