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LLMs can generate operational Docker configurations, but they often miss crucial deployment intents, revealing a significant gap in their utility for production environments.
Forget simple prompt shortening: optimizing tokens for LLM-based database migration is a multi-objective problem where aggressive compression can tank semantic accuracy.
LLMs can evolve code more reliably by translating it into a neutral, behavior-rich specification first, rather than directly transforming code.
LLMs can generate higher-quality code and documentation if you give them a structured architectural context, suggesting a path to more reliable AI-driven software development.
