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> We feel this model excels at instructability which is why we're recommending bringing your own prompt! Sigh Taps the sign: --- start quote --- To put it su

by troupo 2y ago

> We feel this model excels at instructability which is why we're recommending bringing your own prompt! Sigh Taps the sign: --- start quote --- To put it succinctly, prompt engineering is nothing but an attempt to reverse-engineer a non-deterministic black box for which any of the parameters below are unknown: - training set - weights - constraints on the model - layers between you and the model that transform both your input and the model's output that can change at any time availability of compute for your specific query - and definitely some more details I haven't thought of "Prompt engineers" will tell you that some specific ways of prompting some specific models will result in a "better result"... without any criteria for what a "better result" might signify. https://dmitriid.com/prompting-llms-is-not-engineering https://dmitriid.com/prompting-llms-is-not-engineering --- end quote ---

canyon289 2y ago
With open models this isn't as true. The weights are local, you bring your own compute, there's nothing between you and the model. Regarding what is a better result, personally I encourage you to define what a better result is in an evalset and then optimize against that. Agree that having no criteria is not a great situation to be in. This was the main point in a tutorial I did about a month ago now showing how to make a simple AI app using gemma, though the principles hold for any LLM. https://www.youtube.com/live/9zM_93mYdu8 https://www.youtube.com/live/9zM_93mYdu8 Hope this helps!
- troupo 2y ago
  Even then the "You MUST" and "You SHOULD NOT" are just magical incantations that may (and will) randomly fail.
nyrikki 2y ago
In the field of AI wishful mnemonics is the rule, arguments about prescription are of limited value. CoT scratch space extends LLMs from DLOGTIME-UNIFORM TC0 to PTIME with polynomial sized scratch space. https://arxiv.org/abs/2502.02393 https://arxiv.org/abs/2502.02393 Yes prompt engineering is probably better though of as prompt augmentation or stearing. But the systems identification problem and Rice's theorm rigorously debunk the above links core claims. It is a craft that can improve domain specificity and usefulness. All models are wrong (even formalized engineering ones), but some are useful. The price one has to pay when resorting to what is fundamentally compression as PAC Learning is, that it is fundamentally unstable under perturbations. You are basically searching through a hay stack with a magnet, and making sure that at least one of the needles you find is the correct one is a symantic property. Guiding the approximate retrieval process to improve your results will always be a craft. The snake oil is mostly on the side that claims that unrestricted natural language is a possibility. We still only have NLP, and true human level NLU is still thought to be beyond the limits of computation IMHO. Thus prompt augmentation is a consequence of the argument that link was trying to make.

by troupo 2y ago

canyon289 2y ago

With open models this isn't as true. The weights are local, you bring your own compute, there's nothing between you and the model. Regarding what is a better result, personally I encourage you to define what a better result is in an evalset and then optimize against that. Agree that having no criteria is not a great situation to be in. This was the main point in a tutorial I did about a month ago now showing how to make a simple AI app using gemma, though the principles hold for any LLM. https://www.youtube.com/live/9zM_93mYdu8 https://www.youtube.com/live/9zM_93mYdu8 Hope this helps!

troupo 2y ago
Even then the "You MUST" and "You SHOULD NOT" are just magical incantations that may (and will) randomly fail.

nyrikki 2y ago

In the field of AI wishful mnemonics is the rule, arguments about prescription are of limited value. CoT scratch space extends LLMs from DLOGTIME-UNIFORM TC0 to PTIME with polynomial sized scratch space. https://arxiv.org/abs/2502.02393 https://arxiv.org/abs/2502.02393 Yes prompt engineering is probably better though of as prompt augmentation or stearing. But the systems identification problem and Rice's theorm rigorously debunk the above links core claims. It is a craft that can improve domain specificity and usefulness. All models are wrong (even formalized engineering ones), but some are useful. The price one has to pay when resorting to what is fundamentally compression as PAC Learning is, that it is fundamentally unstable under perturbations. You are basically searching through a hay stack with a magnet, and making sure that at least one of the needles you find is the correct one is a symantic property. Guiding the approximate retrieval process to improve your results will always be a craft. The snake oil is mostly on the side that claims that unrestricted natural language is a possibility. We still only have NLP, and true human level NLU is still thought to be beyond the limits of computation IMHO. Thus prompt augmentation is a consequence of the argument that link was trying to make.