{"id":42,"date":"2026-09-02T16:10:15","date_gmt":"2026-09-02T16:10:15","guid":{"rendered":"https:\/\/blog.avaloncompany.ai\/2026\/09\/your-model-is-not-being-vague-your.html"},"modified":"2026-10-08T08:06:00","modified_gmt":"2026-10-08T08:06:00","slug":"your-model-is-not-being-vague-your","status":"publish","type":"post","link":"https:\/\/avaloncompany.ai\/blog\/2026\/09\/your-model-is-not-being-vague-your.html","title":{"rendered":"Your Model Is Not Being Vague. Your Request Is Underspecified."},"content":{"rendered":"
START HERE<\/div>
Your Model Is Not Being Vague. Your Request Is Underspecified.<\/div>

Follow the steps below and use the linked tools and references as you go.<\/p>

<\/div>

Evidence trail: Products<\/p><\/section>

What this note is about<\/h2><\/div>

Here is a complaint you have either made or heard this month: the answer was fine, but it was generic, and by the time I fixed it I could have written it myself.<\/em><\/p>\n

It is worth taking that complaint seriously, because it is usually filed against the wrong defendant. The model is not hedging. It received a request with no stated role, no context, no constraints, no required output shape and no way to check its own work \u2014 so it returned the statistically safest thing that satisfies all possible readings of that request. Which is, necessarily, the blandest one.<\/p>

Read the full operating note<\/summary>
\n

The interesting question is not “which model is less generic.” It is: what is the minimum specification that makes a generic answer impossible?<\/strong><\/p>\n

Five slots, one page<\/h3>\n

The answer turns out to be small, and it is the same five slots every time.<\/p>\n

Role.<\/strong> Not “you are a helpful assistant.” The specific job that produces the specific register: the person who reads service contracts for a living, the person who writes cancellation letters that get honored.<\/p>\n

Context.<\/strong> The facts that constrain the answer. Amounts, dates, what was already tried, what the other party has said. Most underspecified prompts are missing this one, and it is the single largest cause of output you have to rewrite.<\/p>\n

Constraints.<\/strong> What must not happen. No invented figures. Do not soften the request. Under 150 words. Constraints are what convert a plausible draft into a usable one, because they are the only part of the prompt that can be violated visibly.<\/p>\n

Output shape.<\/strong> A table with these columns. Three options, each one paragraph. An email with a subject line. When you do not specify shape, you are asking the model to guess, and its guess is an essay.<\/p>\n

A check.<\/strong> The step almost nobody includes: ask the model to list what it assumed, or to mark which claims came from your source and which did not. This is the difference between a draft you skim and a draft you can actually verify \u2014 and it is what makes the pattern safe to use on documents where being wrong costs money.<\/p>\n

Fill those five slots and the same model that gave you mush gives you something you can send. Not because it got smarter. Because you stopped asking it to guess.<\/p>\n

Where this stops being a party trick<\/h3>\n

The reason to learn a format rather than collect prompts is that the format survives contact with the tasks you actually dread:<\/p>\n