Moving a large language model (LLM) prototype into production requires a comprehensive checklist that goes beyond basic technical feasibility. A CTO must ensure the entire application delivers reliable results, protects sensitive data, and operates within performance and cost limits. This involves evaluating twelve key areas, including architecture, data, model evaluation, security, and ownership, with measurable acceptance criteria and responsible engineering owners for each. The ultimate goal is to establish the organization's capability to operate, monitor, maintain, and improve the system post-launch, ensuring acceptable business outcomes under realistic conditions. AI
IMPACT Provides a structured approach for engineering leaders to transition LLM prototypes into reliable, production-ready applications.
RANK_REASON Article provides practical guidance for engineering leaders on deploying LLM prototypes, fitting the 'tool' category for practical application guidance.
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