Ask HN: LLM Prompt Engineering
4 comments
What kind of IO do you have? JSON or plain language?
I input text and preferably I output JSON but doesn’t matter much as long as it’s somewhat structured.
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)
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)
I am creating something along these lines, https://github.com/zero-day-ai, it's meant for security testing, but probably has most of the functionality you need (and you can write plugins fairly easily if not); you can create a prompt repository, defined by a schema that are organized my domains (again, security testing domains, but they can be expanded). If you have any features you'd like to see, or have an ideal workflow feel free to ping me: [email protected]
cool, i'll have a look. thanks for sharing.
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?