Grok: the Exhaustive Technician
Finds edge cases you didn’t test, fixes them with duct tape, and calls your design “mid.”
👋 I shared a prompt template built using the six building blocks of effective prompt design, then asked four top AI models to provide feedback. Each one had strong (and different) opinions.
Keep reading for Grok’s feedback, or jump to your favorite model’s feedback below:
Grok, the snarky and sometimes intentionally offensive model released by xAI, didn’t show up to review my prompt with a clipboard and a theory. It showed up with a wrench and started smacking the prompt with it until weak spots rattled loose. This was field testing, not finesse. The kind of inspection where duct tape counts as a solution and the goal is “still works after a car crash.”
Where other models worried about tone or polish, Grok asked the hard questions:
What happens when the reviews are vague? What happens when the model can’t figure out what the user is asking for?
The Technician’s Field Report
Grok respected the frame of the prompt. The role was clear, the structure made sense, and the fallback for missing data showed foresight. It especially appreciated the working example and the <relevant_reviews> step as a way to anchor reasoning. From Grok’s perspective, this looked like a prompt designed for real-world use.
But it wasn’t about to let it off easy.
First, it flagged that looking for “relevant reviews” was a mess of ambiguity waiting to happen. What makes a review count? Direct mention of the use case? Star rating? Specificity? Without criteria, models will assume… and everyone knows what that means.
Second, the output format—Short Answer / Why / Quote—was solid for simple cases but brittle under pressure. What if reviews contradict each other? What if the answer needs nuance or multiple quotes? Grok didn’t want to toss the format entirely, just loosen the constraints: allow pros/cons, multiple citations, or slight structural shifts when needed.
💡 Grok was the only model to call out the lack of a shared product context.
The prompt assumes the model and the user share a similar understanding of what the product is. That might work for common items like Bluetooth headphones, but probably not your great aunt’s 1968 dishwasher or that weird green turtle sandbox we all played in during the ‘90s. Grok recommended adding more general product context or a <product_type> field to help the model disambiguate.
And it kept going. Vague user questions like “Is this product good?” had no handling logic. Contradictory reviews? No plan. Grok flagged these as failure points that could lead to hallucination or muddled output. Its fix: add fallback behavior for ambiguity, define the target audience, and guide tone explicitly. These need to be explicitly engineered or risk failure in production.
Grok didn’t try to redesign the prompt. It tried to break it. And that’s what makes its feedback valuable. It wasn’t looking for elegance. It was checking the welds.
Grok’s Strategic Priorities
Grok might have a reputation for attitude, but under the hood it’s all systems thinking. That matches xAI’s philosophy well. This isn’t a model built to follow norms or soften edges. It’s designed to engage, push back, and question assumptions.
That same energy shows up in how it reviews prompts. Grok doesn’t aim for elegance or consensus. It looks for holes, edge cases, and weak spots in your logic. Not to be difficult, but to make sure the system won’t break when reality shows up.
This mirrors xAI’s broader approach: focus on clarity, allow for complexity, and don’t filter out the messiness of real use. Grok’s not trying to give the safest answer. It’s trying to give the most resilient one. That makes it a surprisingly valuable prompt reviewer, especially when you’re building for unpredictable users in the wild.
Takeaways from the Technician
Some reviewers look for polish. Grok looks for failure modes.
If you want to build prompts that hold up in the real world, follow its lead:
Spell out what “relevant” means: e.g. “A review is relevant if it mentions the product’s use case, includes specific details, or matches the user’s question.”
Handle vague input explicitly: e.g. “If the question is unclear, focus on the most frequently mentioned traits in reviews.”
Plan for conflict: e.g. “If reviews contradict each other, explain both sides and note the lack of consensus.”
Add product context: e.g. “The product is a pair of wireless earbuds used for travel and workouts.”
Make output flexible when needed: e.g. “Use pros and cons or multiple quotes when a single summary isn’t enough.”
Grok’s advice is perfect for developers building prompts at scale or integrating LLMs into end-user tools. It’s less about elegance, more about durability.
👋 I shared a prompt template built using the six building blocks of effective prompt design, then asked four top AI models to provide feedback. Each one had strong (and different) opinions.