LLMs have trouble counting the number of words in their responses. When asked for an answer with 500 words, they often provide 300 words. When asked for 1,000 words, often less than 500 are returned. What tips and techniques help guiding an LLM towards a response of a longer length? (I have tried …

** temperature or max tokens ** so how big of responses are u usings Couse there’s a couple methides I’ve used in the past such as chunking it or just making a directory for the AI’s api… and they both have their tradeoffs as well like chunking can result in super large prompt responses and can…

Hiya, I’m just guessing, but perhaps the model is just answering your question in the most succinct way possible. Why use 500 words when 300 words will do? One simple way to get it to produce the proper length of words is to explain to it why it’s necessary. “Can you please produce me a blog ar…

OK… great example i was working on a sub directory by importing folders and content as reference and the ai in the api will understand if u put the correct coding like i made this real quick for an example. is not working but can make it work “”" import json def import_documents(file_path): wi…

Maybe the problem is actually in the approach. Why would 1000 words be needed if 500 does the job for a specific task? If you want longer output you prob need to enrich your instruction so the LLM has more to talk about. I usually prompt for an outline first, then ask it to make the outline more …

How to guide GPT towards answers of a certain length?

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How to guide GPT towards answers of a certain length?

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