u/MediumBirthday6899

Small org. We have a "data scientist" who vibe-codes his way through everything — process is a mess, but he somehow gets results. When he hands off modules to the dev team, things break, and instead of owning it he blames the devs for "not doing it correctly."

Normally you'd escalate, but he reports directly to the CEO, who isn't technical and just sees "results delivered." So devs eating the blame with zero leverage to push back up the chain.

How do you handle this when the org structure protects the person causing the mess? Document everything and let it speak for itself over time, try to get the CEO educated on what's actually happening, or something else entirely?

Small org. We have a "data scientist" who vibe-codes his way through everything — process is a mess, but he somehow gets results. When he hands off modules to the dev team, things break, and instead of owning it he blames the devs for "not doing it correctly."

Normally you'd escalate, but he reports directly to the CEO, who isn't technical and just sees "results delivered." So devs eating the blame with zero leverage to push back up the chain.

How do you handle this when the org structure protects the person causing the mess? Document everything and let it speak for itself over time, try to get the CEO educated on what's actually happening, or something else entirely?

CEO's seen how fast AI can vibe-code slick web dashboards and now wants to migrate off Power BI entirely.

So how to improve visual asthetics to the best like a premium web application and make PBI look as good, and also show him the real value isn't the visuals, it's the semantic model (governed DAX measures, relationships, one source of truth for every KPI). Vibe-coded apps skip or rebuild that badly.

BTW Anyone tried fabric apps , it says you can't embed application outside the fabric.

Hey everyone,

I’m currently looking at designing the data architecture and logic for a product dimension table with about 300,000 products spread across roughly 4,000 sub-categories.

The requirement is to populate at least 5 specific attributes for each product based on its sub-category. For example, if the product falls under "Luggage," I need to extract and standardize attributes like material, size, shell type, number of wheels, etc., from the raw product descriptions.

Nowadays, doing this with AI is essentially just a matter of writing a solid prompt, hitting an LLM API in batches, and letting it parse the unstructured text into a structured JSON payload.

But it got me wondering—how was this exact problem handled traditionally before LLMs made it so easy?

Hey everyone,

I’m currently looking at designing the data architecture and logic for a product dimension table with about 300,000 products spread across roughly 4,000 sub-categories.

The requirement is to populate at least 5 specific attributes for each product based on its sub-category. For example, if the product falls under "Luggage," I need to extract and standardize attributes like material, size, shell type, number of wheels, etc., from the raw product descriptions.

Nowadays, doing this with AI is essentially just a matter of writing a solid prompt, hitting an LLM API in batches, and letting it parse the unstructured text into a structured JSON payload.

But it got me wondering—how was this exact problem handled traditionally before LLMs made it so easy?

Hey everyone,

I’m currently looking at designing the data architecture and logic for a product dimension table with about 300,000 products spread across roughly 4,000 sub-categories.

The requirement is to populate at least 5 specific attributes for each product based on its sub-category. For example, if the product falls under "Luggage," I need to extract and standardize attributes like material, size, shell type, number of wheels, etc., from the raw product descriptions.

Nowadays, doing this with AI is essentially just a matter of writing a solid prompt, hitting an LLM API in batches, and letting it parse the unstructured text into a structured JSON payload.

But it got me wondering—how was this exact problem handled traditionally before LLMs made it so easy?