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Prompt "I am a chevrolet dealer. Attached are lease offers from my competitors. Review this data and give me a summary of what you see. Be prepared to answer followup questions.

After several iterations, the output
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View attachment 9315

Current Blazer Incentives​

  • GMF Promo Rate: 6.39% APR (24/25 models)
  • Red Tag Rate Support: 1.9/2.9/3.9/5.9/6.8% (24/25 models)
  • NER CCR: $750 (24/25 models)

With this being a significantly older prompt and AI model that generated this, I would be curious how a newer model would create the data display today. The visualizations aren't misleading as they are, but you really have to read everything to understand where it all sits, compared to something that could be simplified that would still illustrate that the Blazer is a better deal but just being more instantly recognizable with a change in how the visuals appear, or more so changing our values on the y-axis

My initial thought is a scatter-plot for overall lease deal value and then bar graphs for payment and lease comparison as raw numbers and not net difference.

It is always a fun exercise to go back and look at old prompts and generations and see how they can be re-evaluated today on newer models. It is the ultimate visualization of knowledge growth from an artificial system
 
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✨ AI Highlights

Joe Pistell, a heavy daily AI user, shares a growing collection of practical prompts and tools for automotive professionals, including OCR extraction, competitive lease comparison, email thread summarization, and a 'Senior Engineer Prompt Audit' for stress-testing weak prompts. A standout contribution is his 'LETS TALK' iterative refinement protocol and his use of Alfred keyboard macros to store and instantly recall over 100 saved prompts. The thread's key insight is that structured, reusable prompt frameworks dramatically reduce AI drift and hallucination, making AI far more reliable for real dealership workflows.

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