Why Your LLM Architecture is Flawed: Mastering Dot-and-Index Paths and Context Isolation in Jev

If you are building production AI systems, you have likely hit a frustrating wall. Your prompts are meticulously engineered, your retrieval-augmented generation (RAG) pipeline is fully operational, and yet your model still hallucinates, drifts off-topic, or gives wildly uncalibrated answers when faced with complex, multi-tenant payloads. The problem isn't your prompt. The problem is your state architecture. In early tutorials, passing a single customer message or a short transaction string into an AI model works seamlessly.