AI does not like leaving blanks.
When a model does not have enough information, it will often still try to produce the most plausible answer.
The result may sound clear, structured, and confident.
But confidence is not proof of accuracy.
This becomes dangerous when AI is used for:
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I gave an LLM 2,000 Excel rows to analyze. Every second or third sentiment label was wrong.
The problem wasn’t necessarily the model.
The problem was the workflow.
LLMs are designed to take instructions and context, recognize patterns, and generate the most useful response they...
Overview
I analyzed 1,906 raw customer reviews to identify what customers complain about and which areas the business should improve first.
Key Deliverables:
Theme & Sentiment Classification: Grouped text reviews into 13 key business themes and calculated the number of negative,...
The most dangerous customer review is not when someone says your app is bad. It is when they start saying your competitor is better. 🛑 That is the moment a complaint stops being just feedback and becomes a churn signal.
For this sample project, I analyzed public customer reviews...
While working on an app review analysis sample, I noticed something important:
Not every negative app review is actually a bug report.
Some complaints point to engineering.
Some point to pricing.
Some point to platform compatibility, UX, support, or content availability.
A few...
