Planning an AI project without a clear evaluation framework is like setting sail without a chart. The work that matters most happens before a single line of code is written. Advance planning is the work to do before the work begins. We agree, and we think most teams skip this step at their own peril.
The practical truth is that evaluation should define your project, not the other way around. Too often, teams fall in love with a model or a tool and then scramble to find a problem it solves. That approach wastes time and erodes trust. Instead, start by asking what success looks like in measurable terms. Is it accuracy on a specific dataset? Speed of inference? User adoption rates? The criteria you set before development will guide every decision that follows. This is not about bureaucracy, it is about clarity. When you know what you are aiming for, you can stop chasing every shiny technique and focus on what moves the needle.
For the readers building or evaluating AI tools, this means embedding evaluation into your workflow from day one. Treat it as a constant feedback loop, not a final exam. Advance planning is the foundation for meaningful assessment. We would add that this approach also protects you from the trap of overcomplicating your metrics. A simple, well-defined success criterion is more powerful than a dashboard full of vanity numbers. If your evaluation plan cannot survive a five-minute conversation with a non-technical stakeholder, it is probably too complex to be useful.
The concrete takeaway is this: before you write your first prompt or train your first model, write down the three questions your project must answer. What problem are we solving? How will we know if we solved it? And what is the minimum acceptable outcome? If you cannot answer those three questions with confidence, you are not ready to start. Advance planning is not a delay, it is the shortest path to a project that actually works.