4 ms·
OP here, I see lots of comments about prompt engineering thinking in the context of asking one question to get the answer. In that perspective, I understand wh
by Oras 4y ago
OP here, I see lots of comments about prompt engineering thinking in the context of asking one question to get the answer.
In that perspective, I understand why many people think it is useless. However, if you tried to make a chain of functions/calls, or worked with a tool like LangChain [0] you will see its importance.
Ex: "Which stock had better performance in the last 6 months, Tesla or Microsoft?"
A question like this would check:
- Understanding this is a financial question.
- Get the stock ticker (symbol) for each one.
- Use an API to get their performance history in the last 6 months.
- Compare.
- Return the answer.
[0] https://github.com/hwchase17/langchain https://github.com/hwchase17/langchain
- throwaway1851 4y agoAnd, with that, we’re back to pre-LLM chatbot design: intent classification, entity extraction, business logic, return a result. Only the whole process rests on a more rickety foundation. It’s also bloated and slow, querying an LLM over and over for these things. I’m starting to see some parallels to modern JavaScript and SPAs. ;-)