[D] Real-time multi-dimensional LLM output scoring in production, what's actually feasible today?

A technical deep dive is exploring whether it's feasible to build a real-time scoring layer that evaluates every LLM output before it reaches an end-user, specifically for regulated industries like finance. The system must operate within a strict sub-200ms latency budget and grade outputs across multiple quality dimensions simultaneously. These dimensions include detecting data exposure (PII, credentials), policy violations, tone/brand safety, bias, and regulatory compliance. The goal is to provide auditable evidence that AI outputs meet strict quality and compliance thresholds, moving beyond simple data leakage checks to assess factual accuracy and adherence to obligations.

While several dimensions appear tractable using techniques like NER, regex, and rule engines, the research hits a major wall with 'hallucination risk' and 'accuracy' scoring. The common 'LLM-as-judge' approach (using tools like RAGAS or Chainpoll) requires a second model call, which destroys the latency budget. Alternative methods like Vectara's fine-tuned cross-encoder are faster but limited in scope. The core question remains: without a ground truth or retrieval context, how can you reliably score the accuracy of an arbitrary AI response in real time? This raises fundamental questions about whether real-time scoring is the right architecture or if asynchronous, retroactive flagging is a more viable production pattern for the hardest problems.