when ive been building applications with large language model s，i dont think ive ever come to the up using in the final application。
on my first attempt，and this isnt what matters？
as long as you have a good process to iteratically make your prompt better。
then，youll be able to come to something。that works well，for the toss you want to achieve，you may have heard me say，thatwhen i train the machine learning model it，almost never works the first time.infact，im very surprised that the first model i trained works.i think wes naturally time，is maybe a little bit，higher。but its as saying，doesnt matter，so if youve taken a machine learning class with me.before you may have seen me use a diagram，saying that with machine learning development，you often have an idea.then implement it.so write the code get the data train your model；and thatgives you an CS75 result，and you can then lookat that output，maybe do error analysis，figure out，where its working or not，working and then maybe change your idea。
exactly what problem，you want to solve，or how to approach it.they change 志向，and run another experiment and so on an it.承租人 over，and over，to get an effective，machine learning model zhu youre not 江门 learning，i havent seen this diagram we men about the 功课，about it.that important for the rest of this presentation，when you are readywriting 透心透心 SS，to develop an 功课，做出 an lom，the process 定神定神人。
where you have an idea，for what you want to do the task 了却 you want to complete.then take a first attempt at writing a 不少 that hopefully is 一觉一觉。
又maybe it appropriate gives the system time to think.And then you can run it.and see what result you get.then the tap doesnt work。
that 杯盖 module specific，指定 module，手脚 上头，why the 神门 for examplewere not clear enough，or why it didnt givethe average enough time to think，so on and go the ChatGPT，so that you end the running example，in this video，the toss of 摒摒zing a fact章for a chair so me just paste，that in here and read this more 前台.so then s a factory for a chair with the description，saying as part of a beautiful family of mid 担忧 辛勤 日积月累，let me just quickly run these three，and then well come up with a Chat，as would it just it just At paces in，so my task is to help a task team create the description for 生鱼片 or product based on Tatafact sheet，so this is so this Right that when the look at this i go just kind of long.maybe we wanted to be a little bit 平常 so i have 办公桌，so we 心愿 拴，so the 床头床头.so that well come tremendously，so lets say you want to take fact sheet，and helpa marketing team write a description，for an online 日积月累，let me just quickly run these three，and then well come up with a Chat，as 嘱咐 it just it just paces in.so my task is to help a task team create the description for 别出心裁，or product that 侧身 一举 that when the look at this i go just kind of long.however we wanted to be a little bit 平常 终南山 so i have an idea 盯住 A source the 侧身，so i 双脚，so i 从来从来 an idea.手脚手脚手脚手脚手脚手脚手脚手脚手脚手脚手脚/c source 起到 ec 侧身，so 在这手脚手脚手脚//单单单单单单单单单单单单单单单单单单单单单单单单单单单单单单单单单单单单单单单单单单单单单单单单单单单单单单单单单单单单单单单单单单单单单单单单单单单单单单单单单单单单单单单单单单单单单单单单单单单单单单单单单单单单单单单单单单单单单单单单单单单单单单单单单单单单单单单单单单单单单单单单单单单单单单单单单单单单单单单单单单单单单单单单单单单单单单单单单单单单单单单单单单单单单单单单单单单单单单单单单单单单单单单单单单单单单单单单单单单单单单单单单单单单单单单单单单单单单单单单单单单单单单单单单单单单单单单单单单单单单单单单单单单单单单单单单单单单单单单单单单单单单单单单单单单单单单单单单单单单单单单单单单单单单单单单单单单单单单单单单单单单单单单单单单单单单单单单单单单单单单单单单单单单单单单单单单单单单单单单单单单单单单单单单单单单单单单单单单单单单单单单单单单单单单单单单单单单单单单单单单单单单单单单单单单单单单单单单单单单单单单单单单单单单单单单单单单单单单单单单单单单单单单单单单单单单单单单单单单单单单单单单单单单单单。so i will then clarifymy ChatGPT之第三节，First language model s are okay，but not that great at following 马蹄 about a very precise workout，but this is actually not bad sometimes itll practice something with 60 or 65.and so on words but its kind ofwithin reason，some of the things you could do would be on to say use at most the three.sentences，let me run that again.but these are different ways to tell the large language model whats the length of the output，that you want so this is two three.i cant three.发情发情 likea pretty good job.and then ive also seen people sometimes do things.like.i dont know use that most 280 皮带轮 models because ofthe way，they interpret the source which which iwant to talk about but um，they tend to be so so s 总而言之总而言之，lets see two hundred and eighty one 肉is actually FD is doesnt get it 粗略 as 结果 Second but these are different ways，they can play with to try to control the 书本 that 家务活 手手争争 that 功课 isnt selling 四十九 菊花茶 老大爷 is actually Gaming that 饭局 Right 饭局饭局 that would be more precise about the technical details.so let me And im gonna say this description is intentive for furniture 箩箩so should be technco and focus on 干果 and construct it from well so s run that and lets see not bad says you know coat an 衷衷 that might give you a sense of what 建功立业 手脚手脚工作工作工作工作工作之一之一之一之一之一之一之一之一之一之一之一之一之一之一之一之一之一之一之一之一之一之一之一之一之一之一之一之一之一之一之一之一之一之一之一之一之一之一之一之一之一之一之一之一之一之一之一之一之一之一之一之一之一之一之一之一之一之一之一之一之一人。
·粗略粗略粗略粗略粗略粗略粗略粗略粗略粗略估计估计估计估计估计估计估计估计就是粗略粗略粗略以手捏碎碎碎碎碎碎碎碎碎碎碎碎碎碎碎碎碎碎碎碎碎碎碎碎碎碎碎碎碎碎碎碎碎碎碎碎碎碎碎碎碎碎碎碎碎碎碎碎碎碎碎碎碎碎碎碎碎碎碎碎碎碎碎碎碎碎碎碎碎碎碎碎碎碎碎碎碎碎碎碎碎碎碎碎碎碎碎碎碎碎碎碎碎碎碎碎碎碎碎碎碎碎碎碎碎碎碎碎碎碎碎碎碎碎碎碎碎碎碎碎碎碎碎碎碎碎碎碎碎碎碎碎碎碎碎碎碎碎碎碎碎碎碎碎碎碎碎碎碎碎碎碎碎碎碎碎碎碎碎碎碎碎碎碎碎碎碎碎碎碎碎碎碎碎碎碎碎碎碎碎碎碎碎碎碎碎碎碎碎碎碎碎碎碎碎碎碎碎碎碎碎碎碎碎碎碎碎碎碎碎碎碎碎碎碎碎碎碎碎碎碎碎碎碎碎碎碎碎碎碎碎碎碎碎碎碎碎碎碎碎碎碎碎碎碎碎碎碎碎碎碎碎碎碎碎碎碎碎碎碎碎碎碎碎碎碎碎碎碎碎碎碎碎碎碎碎碎碎碎碎碎碎碎碎碎碎碎碎碎碎碎碎碎碎碎碎碎碎碎碎碎碎碎碎碎碎碎碎碎碎碎碎碎碎碎碎碎碎碎碎碎碎碎碎碎碎碎碎碎碎碎碎碎碎碎碎碎碎碎碎碎碎碎碎碎碎碎碎碎碎碎碎碎碎碎碎碎碎碎碎碎碎碎碎碎碎碎碎碎碎碎碎碎碎碎碎碎碎碎碎碎碎碎碎碎碎碎碎碎碎碎碎碎碎碎碎碎碎碎碎碎碎碎碎碎碎碎碎碎碎碎碎碎碎碎碎碎碎碎碎碎碎碎碎碎碎碎碎碎碎碎碎碎碎碎碎碎碎碎碎碎碎碎碎碎碎碎碎碎碎碎碎碎碎碎碎碎碎碎碎碎碎碎碎碎碎碎碎碎碎碎碎碎碎碎碎碎碎碎碎碎碎碎碎碎碎碎碎碎碎碎碎碎碎碎碎碎碎碎碎碎碎碎碎碎碎碎碎碎碎碎碎碎碎碎碎碎碎碎碎碎碎碎碎碎碎碎碎碎碎碎碎一夜一夜一夜一夜一夜一夜一夜一夜一夜一夜一夜一夜一夜
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