Dev Tools · 2h ago
Why you need a validation layer between LLM output and your code
LLMs can produce valid JSON that is semantically wrong, causing runtime errors. A three-stage pattern—parse, validate, classify—catches structural and semantic issues before code acts on the output. The author provides a TypeScript implementation using Zod schemas.
Meridian48 take
This is a practical, battle-tested pattern that every AI agent developer should adopt, but it's not a silver bullet—validation logic itself can become a maintenance burden.
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Never trust an LLM's output directly. Here's the validation layer I put on every agent. →
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llm-validationai-agents