HN 用户提问：LLM 提示工程
2 分•作者: Scotrix•8 个月前
我目前正在进行一个项目，需要提取用户意图，并将它们转化为确定性的工具/函数/API执行。之后，再通过另一组工具对结果进行提炼/转换。由于收集正确的意图和参数（潜在提示语之间存在很多细微差别）极具挑战性，我使用一长串连续执行的提示语来进行微调，以准确收集执行可靠工具所需的信息。我尝试过使用一些代理框架（包括Langchain/Langgraph），但很快就变得非常混乱，这种混乱很容易产生很多副作用。
因此，我想知道是否有工具、方法或其他任何东西，可以更好地控制LLM执行链，避免最终陷入混乱的配置和/或代码执行实现？也许甚至是一些更可视化的东西，或者只有我一个人在为此苦恼？
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I’m working on a project where I need to extract user intents and move them to deterministic tool/function/api executions + afterwards refining/transforming the results by another set of tools. Since gathering the right intent and parameters (there are a lot of subtle differences in potential prompts) is quite challenging I’m using a long consecutive executed list of prompts to fine tune to gather exactly the right pieces of information needed to have somewhat reliable tool executions. I tried this with a bunch of agent frameworks (including langchain/langgraph) but it gets very messy very quickly and this messiness is creating a lot of side effects easily.<p>So I wonder if there is a tool, approach, anything to keep better control of chains of LLM executions which don’t end up in a messy configuration and/or code execution implementation? Maybe even something more visual, or am I the only struggling with this?