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Determining choosing the right LLM customization method for production requires understanding your data, budget, and performance constraints in detail. Organizations often struggle with LLM deployment because generic models don't capture proprietary knowledge, industry terminology, or task-specific reasoning patterns needed for competitive advantage. Fine-tuning excels when you have stable training data and can afford GPU resources and model hosting; RAG works better when your knowledge base changes frequently or you want to reduce computational overhead. The article walks through cost comparisons, latency tradeoffs, and implementation complexity for each pathway. Media buyers, product teams, and ML engineers implementing retrieval systems will recognize immediate parallels to their own infrastructure decisions. Making this choice upfront shapes your entire development roadmap, team skill requirements, and long-term maintenance burden.