A smarter way to approach AI prompting

Generative AI has become a practical tool in search, content, and analytical workflows.

But, as adoption increases, so does a familiar and costly problem: confidently incorrect outputs.

Also called “hallucinations,” the term implies that an AI model is malfunctioning. 

But here’s the truth: This behavior is often predictable and results from unclear instructions. Or, more accurately, unclear prompts.

For example, prompt AI for a “cookie recipe,” and nothing more. Don’t offer details about allergies, preferences, or constraints. 

The result might be Christmas cookies in July, a peanut-packed option, or a recipe so bland and basic as to be unworthy of the name “sweet treat.” This lack of detail can lead to misaligned outputs.

It’s best to expect a model to misbehave and preempt this by creating explicit guardrails. 

This can be done effectively with rubrics.

We’ll examine how rubric-based prompting works, why it improves factual reliability, and how you can apply it to AIs to produce more trustworthy results. 

Fluency vs. restraint: Which is better?

When AI is asked to produce complete, polished answers without specific instructions on how to handle uncertain information or missing data, it often prioritizes fluency over restraint. 

That is, continuing the response smoothly (fluency) rather than pausing, qualifying, or declining to answer when information is missing (restraint). 

This is the moment AI “makes stuff up” – because uncertainty was not established as a stopping point. The consequences can be financially costly and can also harm reputation, efficiency, and trust.

Professional service firm Deloitte was required to repay 440,000 Australian dollars after errors in an AI-assisted government report were found to include fabricated citations and a misattributed court quote, as reported by the Associated Press in late 2025. 

One academic reviewer noted that it: 

• “Misquoted a court case then made up a quotation from a judge… misstating the law to the Australian government in a report that they rely on.”

Should Deloitte have skipped the use of AI? 

Evaluating data and generating reports is an AI superpower. The lesson here is to keep AI in the workflow, but to constrain it – define, in advance, what a model must do when it doesn’t know something.

This is where rubrics enter the fray.

The role of rubrics in AI

It’s common for users to implement generic safeguards against potential patterns of hallucination, but they often don’t hold up in practice. 

Why not? Because they usually describe an outcome and not a decision-making process. This leaves the AI model to make inferences when required information isn’t available.

This is where rubric-based prompting is essential.

A rubric – a scoring guide or set of criteria to evaluate work – can feel like an old-school, academic concept. 

Think of a grid teachers…