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chore: prompt engineering to enhance output stability #119
chore: prompt engineering to enhance output stability #119
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this line is repeated in line 121 - is that intended?
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Yes, we have found that for small parameter models, emphasizing the most important content before and after a long prompt seems to be helpful for the stability of the output.
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Or can we use different prompts for strong inference models such as GPT-4 or small parameter models that need to constantly emphasize requirements? It is true that long prompts may not be necessary for gpt-4, which can already handle it well.
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i think it makes sense to have one prompt for gpt-4 and another for small parameter models
based on the benchmark results from #119 (review) i would recommend updating the examples with the changes i suggested, see if they improve the planning (which they should, since the examples rn are incorrect) and if it does not improve, then use different prompts for the different models 😄 - otherwise, i don't think it is necessary to have different prompts for the models in this PR
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Yes for right now I think we should have a single prompt. Adding more will just add another dimension of complexity which will make it harder to get consistent results.
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Yes, I agree with you. In the case where gpt-4 can already handle the requirements of concise prompts, more examples may only consume tokens. I think two different sets of prompts can be submitted to adapt to gpt. -4 and our local llms