Most AI content online is either too shallow or too model-focused.

You see endless posts about:

“Top 10 prompts”

“Build a chatbot in 10 minutes”

“Use LangChain with 5 lines of code”

“RAG explained for beginners”

That content has value, but it misses the harder question:

How do you actually design AI systems that survive production?

That is the reason I started building SuperML.dev.

SuperML is my attempt to document the architecture layer behind modern AI systems: agents, ontology, NL-to-SQL, RAG, model governance, inference routing, semantic layers, model risk, and production-grade AI engineering.

The Real AI Problem Is Not the Model

In enterprise AI, the model is rarely the whole system.

The real problems are usually around:

How data is interpreted

How prompts are governed

How agents are registered and monitored

How business meaning is mapped to raw systems

How model calls are routed and controlled

How hallucinations are detected before damage happens

How AI systems are explained to…