在生产中失败的 7 种 LLM 集成模式

📄 中文摘要

经过 18 个月的 LLM 集成实践,发现一些模式在生产环境中经常失败,尤其是对 JSON 模式的过度信任。许多人认为 JSON 模式意味着始终返回有效的 JSON,但实际上它只是尝试生成 JSON,仍然需要进行有效性验证。未能进行适当的验证可能导致系统出现意外错误,影响整体性能和用户体验。通过分析这些真实的失败案例,可以更好地理解在生产环境中实施 LLM 时需要注意的关键问题和最佳实践。

📄 English Summary

The 7 LLM Integration Patterns That Break in Production

After 18 months of integrating LLMs, several patterns have been identified that frequently fail in production, particularly the over-reliance on JSON mode. Many assume that JSON mode guarantees valid JSON output, but it only attempts to generate JSON, necessitating additional validation. Failing to perform adequate validation can lead to unexpected errors, impacting overall performance and user experience. Analyzing these real-world failure incidents provides insights into critical issues and best practices to consider when implementing LLMs in production environments.

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数据源: OpenAI, Google AI, DeepMind, AWS ML Blog, HuggingFace 等