Quick version: paste one prompt, and your AI stops agreeing with your Power BI designs and starts picking them apart instead.
What’s happening here
A developer on r/PromptEngineering shared a prompt that turns ChatGPT (or any LLM) into a senior enterprise BI architect. One who actively challenges your solution rather than rubber-stamping it.
The persona knows Power BI, DAX, Power Query, Oracle SQL, MySQL, and Snowflake. You hand it a design, and it runs a full review: data model quality, SQL logic, performance risks, security gaps, DirectQuery vs Import decisions, refresh reliability. Then it gives you a structured breakdown with a concern level for each area.
That concern level piece matters more than it sounds. Instead of a wall of feedback with no sense of priority, you get a ranked output where the critical issues sit at the top and the minor polish items stay at the bottom. That alone saves you from spending an afternoon fixing something cosmetic while a row-level security gap goes unnoticed.
The idea behind it: most AI tools tell you your design looks good. This one is told its only job is to find problems before they hit production. The framing shift in the system prompt is what drives the different behavior. You are not asking for help. You are asking for scrutiny.
The red team move worth stealing
After the AI gives its first review, hit it with this follow-up:
Now red-team your own answer. Assume the solution you recommended will fail in production. What are the top reasons it could fail?
That second pass is usually more valuable than the first. The initial answer tries to help. The second one tries to break things. That tension is where the real critique lives.
Think about how this mirrors what happens in strong engineering teams. The first design review surfaces the obvious issues. The second pass, after everyone has had time to poke at it, is where someone asks, “but what happens if the source system sends a null in that field?” or “what does this report look like at 50 million rows instead of 5 million?” The AI red-team follow-up recreates that dynamic in a single session. You get both passes without scheduling a second meeting.
💡 Use Cases
- Reviewing code from an offshore team before you sign off on it. You get a structured second opinion in minutes, not days, and you walk into that review with actual questions instead of just a gut feeling.
- Diagnosing a slow Power BI report when you are not sure where the bottleneck is. Paste in your model structure and measure logic, and let the reviewer flag whether the issue is likely at the DAX layer, the data model, or the source query.
- Stress-testing your own architecture before an internal design review. Find the weak spots yourself before your stakeholders do.
- Learning what senior BI reviewers actually look for. If you are earlier in your career, the structured output teaches you what questions to ask on your next build, not just what went wrong on this one.
Prompt of the Day
You need the full master prompt from the original post (it’s long, worth copying). Then use this template each time you want a review:
Project Name: [your project]
Business Objective: [what the business needs to achieve]
Source Systems: [Oracle / MySQL / Snowflake / Excel]
Target Platform: [Power BI / other]
Data Volume: [approximate row counts and table sizes]
Refresh Requirement: [real-time / daily / weekly]
Current Approach: [describe your design]
Known Problems: [performance issues, wrong numbers, refresh failures]
The data volume and refresh requirement fields do the most work here. An architecture that is perfectly fine for 500,000 rows becomes a bottleneck nightmare at 50 million. A design built for daily refresh behaves completely differently when someone asks for near-real-time. Giving the AI those numbers upfront means its feedback stays grounded in your actual constraints, not a generic best-practice checklist.
One tip from the comments: before asking for the full review, ask the AI to hit you with 5 to 10 clarifying questions first. Grain definition, refresh mode, data ownership, row-level security requirements. That first pass makes the actual review sharper because the AI has already surfaced the assumptions it would otherwise make silently.
One honest caveat
A commenter raised a fair point: run the same setup through this prompt 10 times and you will probably get 10 different answers. That is just how LLMs work.
Treat this like a smart colleague giving you feedback, not an audit tool with guaranteed ground truth. Use it to generate hypotheses and catch blind spots, then verify the critical findings yourself or with your team. And as your setup gets more complex, break it into smaller focused prompts rather than one mega-review. One prompt for the data model, a separate one for the DAX measures, another for the security setup. Focused input produces focused critique. A single prompt trying to cover everything at once tends to produce feedback that is wide but shallow.
The core shift is still worth it: make the AI push back instead of agree. That default change alone makes the output more useful.
Follow Cyber Corsairs for more prompts that actually change how you work.
Frequently Asked Questions
Q: Will this prompt produce consistent results every time I run it?
Not reliably, monolithic prompts tend to drift across runs. To fix this, break your prompt into smaller, targeted prompts for each review phase (requirements analysis, data model validation, SQL review, DAX review, architecture critique). Create standards docs that define what “good” looks like, and use an orchestrator prompt to manage the workflow. This forces consistency and makes feedback more predictable.
Q: What should I ask the AI to clarify before it starts the actual review?
Have it ask about: data grain, refresh mode, latency SLAs, row counts, RLS strategy, and data ownership. Without these clarifications up front, the AI will confidently review an architecture based on false assumptions, which completely undermines the value of a senior-level review.
Q: Should I add industry or domain context to the prompt?
Absolutely. Financial systems have different compliance and governance needs than retail or manufacturing. Build domain-specific context into your prompt early so the AI evaluates solutions against the right enterprise standards for your industry, not generic best practices.
Q: How do I get more actionable recommendations instead of a single answer?
Instruct the AI to propose at least 2 alternative designs: one conservative (safer, lower risk) and one aggressive (faster, cheaper). Have it explicitly explain the tradeoffs so you can make an informed decision rather than just accepting a single recommendation.
Q: What expertise areas am I overlooking?
Your technical depth is solid, but add explicit focus on cost implications, vendor lock-in risks, data governance, and compliance, areas that regularly get overlooked in technical reviews but kill projects in production. Make them explicit review criteria rather than afterthoughts.