提示工程的高成本与不可预测性
问题概述
多位用户指出当前提示工程在实际落地中成本高、缺乏可预测性,且企业在测试和验证环节面临显著的资源消耗与不确定性,导致对现有 AI 接口模式的不满。
痛点标签
- 高昂的测试和验证成本
- 缺乏可预测、确定性的 AI 行为
- 碎片化的 AI 接口导致重复解释
- 系统提示的复杂性与维护成本
待验证问题
- 您是否在实际项目中遇到过提示工程的高成本问题?
- 您的团队是否需要更可预测、确定性的 AI 行为?如何实现?
- 您是否希望拥有一个统一的工具平台,而不是多个碎片化的 AI 接口?
证据
原话
I hope the future of AI isn't this sort, where companies provide the user/customer with an interface to an AI that can do things for the user... I would much prefer that companies instead provide an interface FOR an AI, and the user brings their own AI which connects to that interface.In other words, provide my AI with tools, instead of providing me an AI that uses your tools.That way, my AI can bring all the context it needs, and I can bring all of the settings and knowledge about what I want with me. I don't want a fractured world of tons of AIs i interact with where I have to explain all the fundamental information about what I want and how I work every time.
原话
This is an amazing article. The problems it describes are exactly what we found when building a production system that used LLMs to (most of the time) produce reliable results. Extensive tests are necessary, and stakeholders have no idea how their suggestions fail in production. They just see the handful of times they tried something and had it work, not the long tail of cursed results.
原话
One issue with this that we ran into is that it costs actual countable money to run the test suite, which is distinct from anything else I’m used to, so the notion that we’d do enough testing to generate a statistically significant gauge of performance - man, I know it’s correct, but I’m not sure my company will survive the process.
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