Architecting a Scalable Safety Filter Service for LLMs

This article discusses how to design a production-ready safety filter microservice for large language models (LLMs) that enforces policy decisions at web scale, maintains tight latency budgets, and routes ambiguous cases to stronger detectors or human reviewers.

Details

The article outlines a scalable architecture for a safety filter service that can handle large language models (LLMs) at web scale. The key components include an ingress/pre-check stage with deterministic rules, a fast classifier for initial binary/label decisions, an LLM safety check for nuanced taxonomy decisions, a human-in-the-loop (HITL) queue for ambiguous cases, and a policy engine that maps taxonomy and confidence to actions. The article emphasizes the importance of per-category thresholds, soft blocks, and explainable decisions to balance business constraints and legal risks. It also provides guidance on model selection and training, recommending a mix of rule-based heuristics, compact transformers, and instruction-tuned safety LLMs. The key is to find the minimum complexity that achieves precision targets while also being able to detect model drift once deployed in production.