How I Built a Production RAG Agent for Healthcare Claims Analysis (With Guardrails and Evals)

2026-07-09 · Source: Machine Learning on Medium · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Software Development & Engineering · Depth: Intermediate, medium

Summary

HealthClaim Copilot is a production-ready RAG agent designed for healthcare insurance claim analysis, built using LangGraph, Qdrant, and Groq. This system addresses the challenge of manual claim denial reviews by providing a natural language interface for querying claims data, aiming for grounded, cited answers in under 2 seconds. Its architecture features six layers: input guardrails for PII and scope, a router to direct queries to RAG or SQL nodes, a synthesizer for combining results, and output verification for hallucination detection. The agent utilizes 500 synthetic CMS-style claims, BAAI/bge-small-en-v1.5 embeddings via fastembed, and Groq's llama-3.1-8b-instant LLM. Evaluation metrics include Answer Relevance (0.779), Faithfulness (0.494), and Route Accuracy (0.900), yielding an Overall score of 0.724. The entire system is deployed on a free stack including Render and Vercel, highlighting efficient resource use with fastembed for embeddings.

Key takeaway

For AI Engineers building RAG agents for sensitive domains like healthcare, prioritize a multi-layered architecture with robust guardrails and intelligent routing. You should implement PII detection and output verification early, and conduct thorough evaluations with a golden dataset before deployment. Consider fastembed for memory-efficient embeddings to enable cost-effective, free-tier deployments, ensuring your system is both safe and performant. This approach minimizes risks and improves answer quality.

Key insights

A robust RAG agent for sensitive domains requires multi-layered guardrails, intelligent routing, and rigorous evaluation for production readiness.

Principles

• Intelligent routing enhances RAG answer quality.
• Guardrails are non-negotiable for sensitive data.
• Evals must precede production deployment.

Method

Build a 6-layer RAG agent: input guardrails, LLM router (RAG/SQL), RAG/SQL nodes, synthesizer, and output verification. Orchestrate with LangGraph, using fastembed for embeddings and Groq for LLM calls.

In practice

• Implement PII and hallucination guardrails.
• Use LangGraph for agent orchestration.
• Opt for fastembed on constrained infrastructure.

Topics

• RAG Agents
• Healthcare AI
• LangGraph
• LLM Guardrails
• Qdrant
• RAGAS Evaluation
• fastembed

Code references

• Sakshi3027/healthclaim-copilot

Best for: AI Engineer, Machine Learning Engineer, MLOps Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by Machine Learning on Medium.