Cost & Latency Controls

RAG sounds complicated, but the idea is simple: before an AI answers, it first looks up useful information from a knowledge source and gives that information to the language model. This guide explains RAG, LLMs, state, memory and tools using one simple mental model.

A larger context window does not guarantee a better answer. This article explains how signal dilution, conflicting evidence, stale state, position sensitivity, and lossy compression can reduce AI reliability—and introduces a practical Context Pressure Test.

Private AI infrastructure should not be designed around one GPU or one model. A more resilient approach combines fast inference GPUs, memory-rich AI systems, physical-AI nodes and optional frontier cloud models behind a capability-aware routing layer.