may 5, 2026 · 1 min read · rag, knowledge-graphs, architecture
Context isn't retrieved. It's assembled.
Similarity is not understanding. Why AI integrations that shine in demos fall over in production.
Most AI integrations feel brilliant in demos and useless in production.
Here's why.
Everyone reaches for RAG. Chunk the data, embed it, retrieve by similarity. It's clean. It's fast. And it completely misses the point.
Similarity is not understanding.
When someone asks "why did we make this decision?" or "who owns this system?", the AI doesn't need similar text. It needs relationships. It needs to know this decision connects to that person, that meeting, that outcome.
It needs structure, not proximity.
The moment you stop thinking in documents and start thinking in graphs, everything changes. The AI stops searching and starts reasoning.
That's not a model problem. That's an architecture problem.
Context isn't retrieved. It's assembled.
Most teams are one mental-model shift away from building something that actually works.
Originally posted on LinkedIn.