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GenAI projects fail at an 80% rate not because models aren't capable, but because teams skip production-ready infrastructure. Critical components include provider abstraction layers, rate limiting, caching, version-controlled prompts, structured error handling with fallbacks, and automated prompt testing. A production GenAI system requires at minimum 7 key folders: config management, LLM client abstraction, utilities (rate limiter, cache, logger), prompt versioning, error handlers, and tests. The difference between demos and production systems lies in implementing graceful degradation patterns where every layer has a fallback strategy.