LLM deployment best practices: Business needs first, model selection second

Developing and deploying a successful LLM solution requires a structured approach, prioritizing business needs over model selection. The process involves five key steps: understanding the business problem and defining both model-specific and business-centric metrics, preparing by exploring non-LLM alternatives and evaluating candidate models on factual attributes before relying on benchmarks, selecting the most suitable model, customizing it for specific tasks, and finally, productionizing it for real-world use. This methodical sequence ensures that LLM projects are aligned with business objectives and are practical to implement and maintain. AI

IMPACT Provides a framework for businesses to successfully integrate LLMs by focusing on problem definition and practical implementation over premature model selection.

RANK_REASON The item provides advice and best practices for LLM development and deployment, rather than announcing a new release or significant industry event.

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• Accuracy
• BLEU
• exact match
• F1
• Gradient Boosted Trees for Corrective Learning
• LLM
• Perplexity
• regular expression

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