Top 20 Guardrails to Secure LLM Applications
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Ensuring the secure, ethical, and reliable deployment of Large Language Models (LLMs) requires robust guardrails. Key strategies include filters for inappropriate and offensive content, shields against prompt injections, validators for response relevance and URL availability, and tools for maintaining the quality of language output. Additional measures like competitor mention blockers, price quote validators, and logic consistency checkers are essential for maintaining integrity and functionality in LLM applications.
Table of contents
Security and Privacy GuardrailsResponse and Relevance GuardrailsLanguage Quality GuardrailsContent Validation and Integrity GuardrailsLogic and Functionality Validation GuardrailsSort: