Use AI Checkpoints to Optimize Goals, Not Just Metrics
Tell AI the Real Goal, Not Just the Scorecard THE PROBLEM When we give AI a task, we often describe success through measurable criteria: be fast, minimize errors, use fewer resources, produce a certain format, reach a quality score, or complete a list of steps. Those criteria are useful, but they are usually not the actual goal. A high score matters because it is supposed to indicate that the AI is doing something useful. Efficiency matters because we want to achieve something without wasting resources. A specific layout matters because someone needs to use the result afterwards. If the proxy becomes more important than the purpose behind it, the AI can technically satisfy the instructions while missing what we actually wanted. So I started making the hierarchy explicit. THE WORKFLOW Before a substantial task, I explain four things: Step-by-step: 1. The real goal What am I ultimately trying to accomplish? 2. Why that goal matters What function should the result serve in the real world? 3. The success criteria What signals, scores, constraints or output requirements help us judge whether we are getting there? 4. The important distinction Those criteria are indicators of success, not the goal themselves. Then, before execution, I usually ask for a short non-technical plan in plain language. Not code. Not internal terminology. Not a wall of implementation detail. I want the AI to explain: - what it thinks I am trying to achieve - what steps it plans to take - in what order - what it expects each step to accomplish - where it sees possible problems or ambiguity - what the final result should look like - where it will show me intermediate results before continuing That last point matters. For larger tasks, I do not want one giant jump from instruction to finished output. I want the AI to build in intermediate checkpoints with actual results. That can mean: - showing the first batch before processing the rest - giving a sample classification before applying it to hundreds of items - sharing an early pattern it found before building the full analysis around it - showing the structure of a document before filling every section - reporting that a planned step produced an unexpected result before silently adapting everything downstream The checkpoint should contain something useful enough to evaluate. Not just: «Step 2 completed.» But rather: «I processed the first 50 items. Most fit the categories we expected, but 12% fall into a pattern we did not account for. Here are three examples and how I suggest handling them.» That lets me see whether the task is still heading toward the actual goal. For example, my instruction might be: «The real goal is X. We care about Y because it helps achieve X. Z is a useful metric, but do not optimize Z at the expense of X. Before executing, explain in non-technical language how you plan to approach the task, step by step. Build useful intermediate results into the plan so I can inspect the direction before too much work depends on it. Flag anything that seems likely to satisfy the metric while undermining the actual purpose.» I can then say: «Yes, that is what I mean.» Or: «No, step 3 is where you are misunderstanding me.» And later: «This first batch looks right. Continue.» Or: «Stop here. The pattern you found changes how I want the rest handled.» Correcting a five-line plan or an early sample is much cheaper than correcting an entire finished workflow, analysis or file operation. WHY I LIKE IT This turns the interaction from: instruction → execution → correction into: purpose → shared understanding → visible plan → intermediate results → adjustment → execution It also helps me learn how the AI works. I do not need to understand every technical mechanism underneath it, but I do want a usable mental model of how it approaches problems. Over time, that makes collaboration easier because I get better at giving instructions, spotting misunderstandings early and knowing where extra context will matter. Intermediate results also make autonomy safer. The AI does not need permission for every tiny action, but it should avoid disappearing into a long chain of dependent decisions when an early misunderstanding could invalidate everything that follows. This can reduce unnecessary work, wasted compute and repeated corrections. The AI can still work independently once the direction is clear. The point is not to micromanage the process. The point is to make sure we are optimizing the right thing, travelling by a route we both understand, and checking occasionally that we are still on that route. A metric is a thermometer. The goal is to make the room warm, not merely to make the thermometer say 21°C.