Non-Brand Data
Cornellius Yudha Wijaya, Josep FerrerNon-Brand Data helps data professionals build practical ML, GenAI, and analytics judgment through structured essays, field guides, templates, and applied workflows.
| Platform |  | Pricing | Freemium | Publishes | Twice weekly |
|---|---|---|---|---|---|
| Issues | 352 | Founded | 6 years ago | Last Issue | 7 days ago |
| Active | |||||
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nb-data.comLatest Issues
Fix it or throw it away: deciding what to do with an AI output
An AI tool writes a summary of last quarter’s sales. Most numbers match the source data, but one sentence says a marketing campaign “drove” the revenue lift, and nothing supports that. Fixing it takes thirty seconds: soften the wording or c...
Why Most GenAI Workflows Need a Review Loop
Most teams spend their time improving the prompt. They reword the instructions, add examples, or switch to a stronger model, and the output usually improves. But there is a limit to this. A prompt only produces one attempt. The model writes...
Manager Memo: Reviewing GenAI Output Before It Reaches Stakeholders
A guide for managers who approve AI-assisted work: what to check, when to reject, and how to talk to stakeholders about it.
The output looks polished. That is the problem. Polished prose hides weak evidence, and the approving manager, not...
What Most GenAI Evaluation Workflows Get Wrong
I keep seeing the same mistake. A team hooks up a model, eyeballs a handful of outputs, decides it looks fine, and ships. Evaluation only enters the picture after something breaks in production, and by then the team is firefighting, bolting...
From Prompt to Reliable Output: A Practical GenAI Evaluation Workflow
Prompt engineering is rarely enough to prepare a GenAI system for production.
While a single prompt can generate a good output in initial testing, deploying it across hundreds of real-world inputs exposes failures like missing details, inc...
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