RAG isn’t a feature.
It’s an architecture.
Most people think it’s:
“Add docs → retrieve → generate”
That works… for demos.
But in production? That’s where it breaks.
Real-world RAG looks like this:
• Re-ranking → filters better context
• Hybrid search → semantic + keyword = higher accuracy
• Multimodal → text, PDFs, images, audio
• Graph RAG → relationships > raw chunks
• Agentic RAG → AI decides *how* to retrieve
• Multi-agent → retrieve, verify, summarize separately
The shift is simple:
❌ Basic RAG → fetch & hope
✅ Advanced RAG → retrieve, refine, reason
Because at the end of the day:
Better context = better answers.
So the real question is:
Are you still building demo RAG…
or production-grade systems? 👀
#AI #RAG #LLM #GenAI #AIArchitecture
Postgres is bad at handling lots of connections.
It's one of the biggest pain-point I've seen from customers over the past year.
PgBouncer is the way to solve this. It's powerful, but also easy to misuse. So, I wrote about it.