Recognize JSON Bleed as a Common LLM Production Failure

LLMs confuse internal reasoning with final output, exposing metadata such as intent: billing_query confidence: 0.91 escalate_flag: false response_text: I'd be happy to help with that! directly in customer chats. This happens because structured output prompts lack robust defensive parsing, and LLMs occasionally vary their formatting, bypassing expected JSON extraction.

Fix by Enforcing Strict Output Parsing

Treat any deviation from expected structure as a bug. Implement parsing that strips or hides internal tokens before user delivery—don't rely on the LLM always adhering to your prompt. This prevents screenshots going viral with captions like 'the AI is glitching lol,' forcing unplanned explanations to product managers.

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