Self-Correcting Knowledge Bases for Healthcare: How LUMOS Agents Prevent RAG Drift

Introduction: The Silent Problem of Knowledge Base Drift in Healthcare Every time a major LLM update rolls out—be it from ChatGPT, Perplexity, or Gemini—your organization’s embedded RAG knowledge base faces a hidden risk: drift. In healthcare, where accurate citations to clinical guidelines, drug interaction protocols, and HIPAA-mandated documentation are non-negotiable, drift can lead to compliance failures and patient safety issues. Periodic manual audits are slow, expensive, and often miss subtle shifts in how the LLM retrieves and cites content. Enter the LUMOS multi-agent framework —a closed-loop system designed for enterprise operations leaders who need their AI-powered knowledge bases to stay accurate without constant human oversight. This article walks through the two-agent architecture (Drift Detector and Refresh Orchestrator), explains how to set drift thresholds based on regul

atory materiality, and offers practical steps for validating changes with A/B citation tests while keeping a human-in-the-loop for high-stakes decisions. How Drift Happens in Healthcare RAG Systems RAG (Retrieval-Augmented Generation) is the backbone of AI-powered clinical decision support. It works by embedding documents (like FDA labels or hospital protocols) into vector indices, then retrieving the most relevant chunks when a user asks a question. When an LLM is updated, two things change: 1. Embedding behavior : The model’s embedding function alters how it maps text to vectors, shifting similarity scores even if the underlying documents stay the same. 2. Generation behavior : The LLM’s answer style, citation format, and tendency to summarize or hallucinate can evolve. Together, these shifts cause “citations” to become outdated or unreliable. For example, a query about “warfarin inter

action with NSAIDs” might previously pull the latest 2025 guideline, but after an update, the same vector search could return a 2023 version—or worse, a non-authoritative source. This is the silent drift that can erode trust and compliance. The Two-Agent LUMOS Framework: Drift Detector and Refresh Orchestrator LUMOS solves this by decoupling monitoring from action. Two specialized agents collaborate to maintain citation integrity: Agent 1: Drift Detector The Drift Detector continuously monitors your knowledge base for signs of decay. It measures two key metrics: Embedding similarity drift : How much have the vectors of new queries shifted relative to stored document vectors? A baseline is established after each intentional refresh. A rising divergence indicates the embedding function has changed. Citation accuracy rate : For a sample set of test queries (e.g., the top 100 questions from

clinical staff), the Detector checks whether the top-1 retrieved document matches the expected authoritative source. A drop below a threshold triggers an alert. These metrics are tracked over moving windows (e.g., 7-day and 30-day). The Drift Detector uses statistical process control (SPC) to flag anomalies without generating false alarms from normal query variation. Agent 2: Refresh Orchestrator When the Drift Detector signals that vector shift has exceeded a “safe band,” the Refresh Orchestrator takes over. It does not blindly re-index everything—instead, it performs targeted content updates : Re-embeds only the affected document chunks (identified by comparing old and new nearest neighbor graphs). Updates the RAG index metadata (e.g., publication date, version number) so that future retrievals show the freshest source. Optionally triggers a re-generation of citations in the LLM’s syst

em prompt for that domain. Crucially, the Orchestrator respects a cooldown period (e.g., 24 hours between refreshes) to avoid over-refreshing, which can confuse downstream workflows. Setting Drift Thresholds Based on Regulatory Materiality Not all drift is equal. In healthcare, the materiality of a piece of knowledge determines how quickly you must respond. For example: High materiality : Drug–drug interaction guidelines, adverse event reporting rules, and HIPAA security standards. These require a tight drift tolerance (e.g., < 2% embedding shift or a single mis-citation). Medium materiality : Clinical pathways for common conditions, billing code updates. Tolerances can be wider (e.g., 5% embedding shift, < 5% citation drop). Low materiality : Non-clinical content such as patient education brochures or hospital policies that are not time-sensitive. Tolerances may be 10% or based on a mon

thly batch review. LUMOS allows you to tag each document or chunk with a regulatory materiality level. The Drift Detector uses this tag to adjust its alert thresholds and escalation paths. Orchestrating Refreshes via RAG Index Modifications The Refresh Orchestrator’s actions are not magic—they rely