Ask HN: LLM Prompt Engineering
2 Scotrix 3 9/18/2025, 2:11:27 PM
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.
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?
Ultimately I’d like to extract information like date ranges, specific indications of tool usages (e.g. I have a bunch of data apis with their own individual data and semantic meaning which need to be picked and then a combination of tools to transform the data)