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This comprehensive guide to <a href=https://npprteam.shop/en/articles/ai/how-llms-work-tokens-context-limitations-and-bugs/>https://npprteam.shop/en/articles/ai/how-llms-work-tokens-context-limitations-and-bugs/</a>, equipping engineers and decision-makers with practical knowledge for real-world deployment. Rather than treating language models as black boxes, understanding tokenization, context windows, and inherent limitations lets you design systems that perform predictably and cost-effectively. Whether you're optimizing prompt length for faster inference, selecting the right model size for your budget, or building guardrails against unreliable outputs, these concepts form the foundation of responsible AI implementation. The article connects theoretical concepts to concrete scenarios, making it valuable for anyone moving LLMs from experimentation into production pipelines.