Developer builds custom LangGraph guardrails for RAG systems

This article details how to build input guardrails for RAG systems to prevent prompt injection and the leakage of Personally Identifiable Information (PII). The author explains the risks of PII exposure, malicious prompts leading to instruction bypass or system prompt revelation, and harmful requests. The solution involves creating a custom LangGraph node that uses Microsoft's Presidio library to mask PII and an LLM to check for prompt injection and harmful content. AI

IMPACT Enhances RAG system security by preventing data leaks and malicious prompt exploitation.

RANK_REASON Article describes the implementation of a specific tool/library for AI applications.

Read on dev.to — LLM tag →

• AnalyzerEngine
• AnonymizerEngine
• Guardrails AI
• LangChain
• langgraph
• LLM Guard
• Microsoft
• NeMo Guardrails
• Presidio
• SpacyNlpEngine

• product
• infra

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