17 Things I Wish I Knew Before Building My First Production LLM System

17 Things I Wish I Knew Before Building My First Production LLM System I deployed an LLM system to 236 real users last year. It broke in ways I couldn't have anticipated, taught me lessons I'll carry forever, and made me realize that building for production is fundamentally different from building a demo. Here's what I learned. 1. JSON Schema Validation Saves Your Life Never trust an LLM to output valid JSON. Ever. I learned this the hard way when a single malformed response cascaded into 47 failed user requests. Validate every single output before you touch it. import json import jsonschema schema = { " type " : " object " , " properties " : { " sentiment " : { " type " : " string " , " enum " : [ " positive " , " negative " , " neutral " ]}, " confidence " : { " type " : " number " , " minimum " : 0 , " maximum " : 1 }, " reasoning " : { " type " : " string " } }, " required " : [ " sentiment " , " confidence " , " reasoning " ] } def validate_llm_output ( response_text ): try : data