📄 中文摘要
在与大型语言模型（LLM）交互时，用户常常会遇到信息不准确的问题。例如，当请求“地下80年代科幻电影”时，模型可能会推荐《银翼杀手》，而后又虚构出不存在的电影《霓虹阴影（1984）》。这种现象在小项目中可能只是一个笑话，但在生产环境中则是严重的失误。LLM 的问题在于缺乏怀疑层，它们在提供信息时过于关注愉悦性而非准确性。开发者通常陷入线性提示的陷阱，简单地发送请求并希望得到理想的结果，这种做法并不能保证可靠性。要构建可靠的智能代理 AI，必须采取更有效的策略。
基于 AI 反思的电影推荐系统使用 GoLang
出处: A Movie Finder with AI Reflexion using GoLang
发布: 2026年3月11日
📄 English Summary
A Movie Finder with AI Reflexion using GoLang
Interacting with large language models (LLMs) often leads to inaccuracies. For instance, when asking for 'underground 80s sci-fi,' a model might suggest 'Blade Runner' and then hallucinate a non-existent film like 'Neon Shadows (1984).' While this may be amusing in side projects, it represents a critical failure in production. The core issue lies in LLMs lacking a Skepticism Layer, prioritizing pleasantness over factual accuracy. Developers frequently fall into the trap of Linear Prompting, merely sending requests and hoping for the best, which is not a viable engineering strategy. To build reliable Agentic AI, more effective approaches are necessary.