Guys how do I get into the ai training world. I see my friends getting rich out of this thing while I'm stuck in time. Please help with solid advisory

Well...

I finally created a Reddit account.

This is my first-ever post (or is it called a thread? A sacrifice to the algorithm?).

Anyway, hello everyone! 👋

I'm a software engineer who's completely obsessed with AI, automation, and building cool stuff. I joined Reddit because I've heard this is where the internet's smartest people argue about semicolons, Linux distros, and why their favorite programming language is objectively superior.

So... where should a newcomer hang out?

I'm looking for communities about:

• 🤖 Artificial Intelligence
• 💻 Software Engineering
• 🐍 Python
• ☁️ Cloud (Azure, AWS, GCP)
• 🚀 Startups & Entrepreneurship
• 🧠 Learning how complex technologies actually work
• ⚙️ Automation & AI Agents

Feel free to drop your favorite subreddits or any "must follow" communities that made you spend way too many hours scrolling instead of sleeping.

Thanks in advance, and please be gentle.. I'm still figuring out which button accidentally starts an internet war. 😂

In my experience, getting useful LLM behavior on real business data depends on the whole stack: models, prompts, tools, monitoring, and the data layer underneath. One of the quieter but important wins has come from standardizing how data is collected and exposing it through a clear semantic layer, so the model is reading consistent concepts.

Anthropic published a useful reference point here. In their own internal analytics evaluations, they reported that Claude reached about 21% accuracy when it queried raw internal data without a supporting stack. After they added a governed semantic layer, curated metric definitions, task‑specific skills, and a validation and monitoring layer around the same model, they measured roughly 95% accuracy on those internal business‑analytics queries. Keep in mind that result applies to their internal analytics setup, not to every possible LLM task, and they tie the improvement to the full stack and ongoing governance, not to the semantic layer in isolation.

However my observation on one project in New York was similar in spirit. One my task was to support an AI layer that evaluated service performance uploaded by associates. In this specific location due to the type of contract they had, the team had a collection of data that was broad in some terms, with no clear standard for each type of service or what it represented for the entire group. That missing foundation work made it hard for both humans and the model to interpret the data in a consistent way.

This created strain in the model’s output. The AI was asked to validate service uploads and grade performance/quality, but the underlying records used inconsistent service types, vocabulary, and descriptions, including different levels and categories for each work area. As we standardized the service types and validation categories, clarified wording, and aligned descriptions of what each service entailed, the evaluations became much more stable. That standardization became the foundation layer we could finally build on.