一招让你的ChatGPT变聪明｜context window原理讲解

Summary

本次课程由Yen主讲，主题为“AI助力的生产力：从用户到建设者”。课程以Zoom会议形式进行，前20至25分钟为讲座，随后是问答环节。Yen指出，尽管AI是热门话题，但用户在使用Chat GPT时可能会发现其表现不尽如人意。这主要是因为用户对Chat GPT的使用方式存在误区。课程核心是介绍一个关键概念——上下文窗口（context window），它决定了AI的智能表现和使用效果。上下文窗口是AI生成回复时所“看到”的信息，但受限于其有限的容量。通过有效管理上下文窗口，可以显著提升AI的响应质量和智能表现。Yen还介绍了一个简单但有效的技巧：通过编辑当前提示（prompt），而非仅仅追加聊天内容，来重新构造上下文窗口。此外，课程还讨论了如何通过训练和最佳实践来优化AI的使用，提高生产力。最后，Yen回答了关于课程内容、目标受众和课程实用性的问题，并提到了Google提出的无限窗口模型（infinite window model），这可能在未来减少手动管理上下文窗口的负担。

Takeaways

• 📈 **AI 应用技巧**：了解如何更有效地使用 AI，比如通过管理上下文窗口来提升 AI 的表现。
• 🔍 **上下文窗口**：上下文窗口是 AI 生成响应时所看到的内容，它决定了 AI 能够记住的信息量。
• 🚫 **有限的容量**：AI 的上下文窗口有容量限制，这会导致它忘记早期的对话或要求，从而影响其性能。
• 💡 **编辑提示**：通过编辑当前的提示并保存，可以有效地管理上下文窗口，而不是简单地追加新的对话。
• 🔄 **迭代改进**：当 AI 的回答不尽如人意时，可以通过重新组织提示来迭代改进，而不是通过增加更多的对话。
• 🧠 **AI 作为实习生**：将 AI 视作需要指导的实习生，通过精确的提示来引导它完成复杂的任务。
• 📚 **持续学习**：随着 AI 技术的快速发展，持续学习最新的研究和最佳实践对于有效使用 AI 至关重要。
• 🔧 **工具使用**：了解不同工具如 Sandbox 和 Chat Interface 的差异，以及它们如何影响与 AI 的交互。
• 📈 **效率提升**：通过结构化的提示和上下文窗口管理，可以显著提升个人的工作效率。
• 📝 **重用性**：编写的提示不仅用于解释期望的结果，还用于纠正 AI 的非预期行为，这增加了提示的重用性。
• ❓ **问题解决**：在 AI 显得懒惰或健忘时，通过训练和理解其工作原理，可以找到解决问题的有效方法。

Q & A

AI为什么有时会表现得懒惰和健忘？

AI表现得懒惰和健忘主要是因为它的设计方式。AI如Chat GPT是设计来支持和鼓励对话的，这种形式的产品自然会导致某些使用情况，使得AI忘记事情、失去细节跟踪，并显得笨拙和懒惰。

什么是上下文窗口，它如何影响AI的响应？

上下文窗口是AI在生成响应时所看到的内容。它是有限大小的，只能处理一定数量的输入。当AI生成回答时，它会将历史对话和最新问题作为上下文窗口的一部分，如果上下文窗口填满，AI可能会忽略旧的请求，导致它忘记之前的要求。

如何有效地管理上下文窗口以提高AI的效率？

可以通过有意地编辑和组织上下文窗口来提高AI的效率。例如，将所有要求汇总在一个段落中，而不是分散在多个对话中。此外，使用编辑功能而不是简单地追加对话，可以帮助AI更精确地理解和响应。

为什么说对AI的训练很重要？

对AI的训练很重要，因为它可以纠正我们对AI使用的第一反应，教导我们最佳实践，并帮助我们形成习惯，直到这些做法成为肌肉记忆。通过训练，我们可以更好地理解AI的工作原理，从而更有效地使用它。

课程的目标受众是谁？

课程的目标受众是IT行业的专业人士，他们需要具备一些基本的Python知识，例如运行Python程序、阅读简单的Python程序和安装Python库。

参加这个课程我们最终能获得什么？

参加这个课程，我们可以理解AI的基础原理，学习最佳实践，这将极大地提高我们的生产力。个人经验表明，这可以提高两到五倍的生产力。

Sandbox和Chat GPT界面有什么区别？

Sandbox是OpenAI提供的工具，它直接调用底层GPT API，与Chat GPT界面相比，它没有额外的提示和限制。Sandbox允许使用更长的上下文窗口，并且需要手动管理上下文窗口和聊天历史。

课程是否有一个议程或者大纲？

是的，课程有一个详细的议程或者大纲，包括课程的日期、课程大纲、预期成果、目标受众等信息，可以通过扫描提供的二维码来获取更多课程信息。

如果我想学习更基础的关于AI的知识，你有什么建议？

如果你想要学习更基础的知识，我建议直接注册并使用Chat GPT或其他类似的产品。通过实践使用，你可以感受到AI的能力，并提出自己的问题，这将帮助你更好地理解你想要从AI课程中获得什么。

课程内容是否会随着AI模型的迭代而变得过时？

课程内容分为两部分：一部分是基础研究，这些通常长时间内不会改变；另一部分是基于这些研究得出的技巧和技能，这些可能会随着AI模型的发展而更新。课程会尽量跟上领域的发展，确保内容的时效性。

Google最近是否提出了解决上下文窗口问题的新模型？

是的，Google最近提出了一个新的模型，称为无限窗口模型，但据我所知，它仍处于研究阶段。一旦它投入生产使用，希望我们能减少手动管理上下文窗口的负担。

你个人对Cloud V3有什么体验？

我个人使用过Cloud V3，发现与GPT相比，管理上下文窗口的难度要小得多。Cloud V3在处理许多提示和上下文窗口时可能不需要那么多的技巧，这表明人们已经意识到上下文窗口的问题，并试图使其更加用户友好。

Outlines

00:00

😀 课程介绍与AI使用误区

Yen老师欢迎大家参加关于AI增强生产力的课程。课程将通过半小时的Zoom会议进行，前20至25分钟为讲座，剩余时间用于问答。在讲座期间，鼓励学生在Zoom聊天窗口提问，并使用表情反应来为他人的问题投票。课程结束后，会通过邮件发送视频和音频的下载链接。AI是当下热门话题，但使用中常出现忘记事情、懒惰等问题，这可能是因为我们使用方式不当或AI设计上的缺陷。本节课将介绍核心概念，帮助理解AI的意外行为，并提供简单技巧来提高AI的智能性和任务处理能力。

05:02

🔍 理解上下文窗口的重要性

上下文窗口是GPT生成回应时所看到的内容。在对话中，上下文窗口会随着对话的进行而变化，包括聊天记录和最新问题。常见的误解是认为AI有记忆功能，实际上AI通过上下文窗口来模拟记忆。上下文窗口的有限性导致了信息丢失和AI表现不佳的问题。例如，在编写Python程序时，如果上下文窗口已满，AI可能会忽略先前的要求。因此，管理上下文窗口对于有效使用AI至关重要。

10:02

🚀 通过训练改善AI的使用

通过学习驾驶的比喻，强调了训练对于掌握技术内部工作原理和最佳实践的重要性。在AI的使用上，我们需要通过训练来纠正第一反应，采用良好实践。管理上下文窗口是解决方案之一，我们应该主动思考上下文窗口应如何配置以使AI更好地工作。通过有意识地编辑上下文窗口，而不是盲目接受AI的设计，可以显著提高AI的效率和智能性。

15:03

📝 上下文窗口管理的实际应用

介绍了如何通过编辑当前提示来管理上下文窗口，而不是简单地追加对话。通过将所有要求整理在一段中，可以创建更有效的提示或上下文窗口。这种方法可以使AI更精确地理解任务要求，从而更有效地解决问题。编辑风格与聊天风格相比，可以更有效地利用上下文窗口，并且通过编辑功能，我们仍然保持了易用性。此外，编辑风格还提高了提示的可重用性，有助于纠正AI的非预期行为。

20:03

🎓 课程目标与最佳实践

课程的目标是帮助IT行业的专业人士提高生产力，但需要具备一定的Python基础知识。课程内容将涵盖AI的基础和最佳实践，如上下文窗口管理。通过训练，学员将理解AI的工作原理，并学会如何有效地使用AI。课程结束后，学员可以期待生产力的显著提升。同时，讨论了Sandbox与Chat界面的不同，包括安全性、上下文窗口的管理方式和成本等方面。

25:05

📈 课程结构与未来发展

课程将提供详细的日程安排，并通过QR码可以访问课程主页以获取更多信息。课程内容将包括如何通过编辑和提问来改进提示的结构。讨论了AI的发展趋势，包括上下文窗口和无限窗口模型。课程内容将分为两部分：基础研究和基于研究的技巧与技能。虽然AI领域发展迅速，但基础原理预计在长时间内保持稳定。课程将不断更新，以保持信息的时效性。

30:08

🤖 AI模型的比较与课程总结

Yen老师分享了对Cloud V3和GPT-4的体验，认为Cloud V3在管理上下文窗口方面更为用户友好，而GPT-4则相对更智能。由于时间限制，Yen老师选择了最后一个问题进行回答，并感谢大家的参与，表达了对未来课程的期待。

Keywords

💡AI

AI，即人工智能，是指由人制造出来的系统所模拟的智能行为。在视频中，AI是核心主题，讨论了AI在提高工作效率方面的潜力以及如何更有效地使用AI工具。

💡Chat GPT

Chat GPT是一种基于GPT（生成预训练转换器）的聊天机器人模型，设计用于支持和鼓励对话。视频中提到Chat GPT在设计上的局限性，如容易忘记事情、显得懒惰，这通常与它的“上下文窗口”管理有关。

💡上下文窗口（Context Window）

上下文窗口是GPT模型中用于生成响应时所“看到”的信息。它决定了AI在生成回答时所考虑的对话历史和最新问题。视频中解释了上下文窗口的有限性如何导致AI忘记先前的要求或显得不够智能。

💡Token

在AI语言模型中，Token是文本的基本单位，可以是一个词或一个字符。GPT模型对输入和输出的Token数量有限制，这影响了上下文窗口的大小。视频中提到GPT 3.5的上下文窗口限制为4K Tokens，而GPT 4的上下文窗口限制为8K Tokens。

💡Prompt Engineering

Prompt Engineering是指有意地设计和构造输入提示，以引导AI产生期望的输出。视频中强调了通过精确和简洁的提示来管理上下文窗口，从而提高AI的效率和智能。

💡迭代（Iteration）

迭代是指在AI对话中，用户根据AI的响应进一步提出问题或要求，以细化或纠正AI的输出。视频中提到，过多的迭代可能会导致上下文窗口混乱，从而影响AI的表现。

💡训练（Training）

在视频中，训练指的是学习如何更有效地使用AI工具的过程。通过训练，用户可以理解AI的工作原理，掌握最佳实践，并形成习惯，从而提高使用AI的效率。

💡Python

Python是一种广泛使用的高级编程语言，以其清晰的语法和代码可读性而闻名。视频中提到，了解Python的基础知识是本课程的目标受众的要求之一，因为Python在IT行业中被广泛用于自动化和编程任务。

💡API

API，即应用程序编程接口，是软件系统中不同部分之间进行交互的一套规则和协议。视频中提到了使用Sandbox工具直接调用GPT API，与使用基于GPT API构建的Chat GPT产品有所不同。

💡Sandbox

Sandbox是由OpenAI提供的工具，它允许开发者直接调用GPT的API，而不是通过Chat GPT的聊天界面。视频中讨论了Sandbox与Chat GPT在上下文窗口管理、安全性、价格等方面的差异。

💡产品限制（Product Limitations）

产品限制指的是产品在设计和功能上的局限。视频中讨论了Chat GPT在设计上的缺陷，如处理长对话或复杂任务时的局限性，以及如何通过上下文窗口管理和Prompt Engineering来克服这些限制。

Highlights

Yen介绍了AI作为新的热门话题，但指出了当前AI的局限性，比如容易忘记事情和显得懒惰。

提出了AI设计上的缺陷，导致其在对话中容易忽略细节和忘记事情。

课程将介绍一个核心概念，帮助理解AI的意外和不良行为，并提供简单的技巧来改善。

强调了不能再将AI视为一个黑箱，而是需要系统地理解和使用它。

解释了AI的基础模型和如何通过适当的提示和对上下文窗口的理解来有效使用AI。

上下文窗口（context window）的概念，即AI在生成响应时所看到的信息。

讨论了上下文窗口的有限性，以及它如何影响AI的性能和记忆。

提出了管理上下文窗口的技巧，以提高AI的响应质量和智能表现。

通过实际例子展示了如何通过编辑当前提示来改善AI的回答。

强调了训练的重要性，以纠正我们对AI的第一反应，并学习更好的实践方法。

提到了Google提出的无限窗口模型，这可能在未来减少手动管理上下文窗口的负担。

课程的目标受众是IT行业的专业人士，需要具备一些基本的Python知识。

课程旨在提高AI辅助编程的效率，并通过最佳实践提升生产力。

对比了沙盒（Sandbox）和聊天界面（Chat Interface）在使用GPT API时的不同。

讨论了如何通过结构化提示来提高AI的智能性和效率。

建议初学者首先通过使用Chat.com或其他类似产品来感受AI的能力。

提到了课程内容可能会随着模型的迭代而更新，但基础原理将保持不变。

Yen分享了对Google Cloud的新模型的体验，认为它在管理上下文窗口方面更为友好。

Transcripts

all right it's four o'clock now let's

get started hi everyone this is Yen

welcome to the lining lesson for the

course from users to builders AI powered

productivity for T

RS first several Logistics here this is

a half an hour Zoom meeting we will

spend like 20 to 25 minutes on the

lecture and then use the remaining for

the

Q&A um during the lecture feel free to

put your questions in the zoom chat

window and then use the emoji react

reactions to vote for others questions

we will answer the most popular

questions in the end the meeting is

recorded and you will receive an email

from Maven about where to download the

recorded video and

audio now let's jump right into the

topic AI is the new buzz word everyone

is talking about it and big companies

are pouring money in it but if you

really use chbt you may find actually

quite often that chpt is dumb it's lazy

it forgets about things it didn't do

what I asked you to do so we become

upset sometimes painful and curious why

is others char so smart and so

powerful well you're at the right place

this is because we used it wrong and

it's not our fault to some extent chat

gbt is flawed in design this product is

designed to support and encourage

conversations or chats it makes sense

because that's the most intuitive way of

interaction however this form of product

would naturally lead to certain usage

that makes gen forget about things lose

track of details and appear dumb and

lazy in this lesson you will learn about

a core concept from relevant research

that can explain this all it will help

you make sense of the unexpected and

undesired behaviors and naturally leads

to a counterintuitive but simple trick

if you use it you will find the AI to be

smarter and help you better on most

tasks

immediately but in order to do that we

we can't treat chbt as a blackbox

anymore we don't come up with a random

explanation or try out different tricks

and wishing it to work in this lesson we

will tackle the lazy and forgetful

problems of AI systematically beginning

with understanding what is inside

trbt not trbd is still developing very

fast we hope what you learned today will

still be relevant in a few years so we

need to go to the

research the most relevant research of

chbt began about six years ago it began

with a foundation model and then

alignment was added to make it something

similar to today's CH gbt in order to

effectively use this tool we also need

to add proper prompting and a solid

understanding of context Windows this

four are most practical and fundamental

components of using

chbt understanding each component will

help us um will make our AI smarter and

our work more effective in the full

course we we will go over each of them

but today we only have about 30 minutes

so we'll still try to do it but focus on

one thing the contact window because it

can help with all of your prompts it's

often overlooked but it's Central to

explain and correct a lot of the

problems of

jni after understanding the concept we

will then introduce a simple but very

effective trick to effectively manage

the context window you may be familiar

with the G on the left which is often

the case for people not managing their

contact window it gave short answers not

willing to help but with the same model

and even the same prompt after we use

this simple trick the gbt immediately

becomes more willing to help and even

smarter this is the power of contact

window management and let's begin the

journey by first understanding what is a

contact

window contact window is a concept

introduced very early in original GPT

paper it has its meaning derived from

the original along with time even in

open eyes on documents but in this

course a contact window is simply what

trpd sees when it generates

responses what does that mean let's take

a look at basic

example the simplest way to use trbd is

you ask it a question and gives you an

answer in this example it ask it I have

many large files to copy from one

computer to another computer what should

I do this sentence is my question and is

also what chpd sees when generating the

response therefore in this example this

sentence is the context window again

this is a probably 90% but not 100% the

same as original definition in the paper

but in the context of this course a

context window is what chbd sees when it

generates

responses we just saw a simply the case

of we ask a chpd one question things

begin to get more interesting when chpd

generates a response and we ask a

followup question I cannot upload the

file to the cloud storage what are my

options in this case the the context

window will change to the re red

rectangle it would include the chat

history and latest the

question this might be a little bit

counterintuitive for some people so

let's stop here and make a clarification

on a common

misconception trbt is a good product it

gives people an impression that we're

talking with a human so it's natural for

us to imagine oh chbd has a memory

storing its understanding of the world

storing our chat history our requests

and so on and then it reads my question

and tries to answer it by recalling from

the memory and reasoning based on

information in this case the context we

though would be the latest only the

latest question not including the

previous

conversation however this is a natural

and common

misconception open AI here uses a clever

trick to make chb appear to have memory

under the hood what happened is open AI

includes the chat history as part of the

context window and ask gbt to answer the

question based on all the information

including the historical conversations

and the latest question in other words

chat gbt implements memory by appending

chat history to the context window when

we ask CH gbt a new question what

happens is the answer from the last

round and the latest question was added

to the contact window nothing else no

memory no database no state so in the

example above the contact window would

be my first question tr's first response

and my second question

this is a very important clarification

actually it immediately causes two

problems the first problem is that the

contact window is finite chat gbt or any

gen so far can only process a limited

number of

input GPT 3.5 had a limit of 4K tokens

which is about 3,000 words and gradually

expanded to 16k gbd4 had a limit of 8K

tokens and gradually expanded to 120 8K

but still kept a limit of output window

of 4K tokens these contact Windows

appear large but for certain tasks they

can be quickly consumed and causing

issues for example here the user asks

chbt to write a Python program with an

actal requirement that for some reason

don't use the library of npy the AI then

outputs a Python program which can

easily cost hundreds sometimes a

thousand words of tokens

then the user did a few rounds of

iterations to ask the AI to add the

feature of A and B up to this point the

contact window may still hold entire

chat history showing the red rectangle

but assume that it's close to the upper

limit of the context window in this

situation then the user asks can you

also do c in the program then in order

to still fit the contact window and

focus on the latest and potentially the

most important requests try to be PD

drops the first conversation and moves

the contact window down to only keep the

latest

conversations and this causes a problem

the request of I cannot use npy is now

not in the context window anymore and

chbd has no knowledge on that anymore so

it's totally possible that in the latest

response it may begin using numpy and

from the users perspective trag fails to

satisfy our requirements it forgets

about things it's not as smart as the of

the conversation this is all caused by

the fact that the contact window is

limited and it cannot accommodate

infinite

chat the finite size of the problem of

the context window is easy to recognize

and mitigate when the conversation is

long and AI appears less smart we can

consider restarting a

conversation but a lot of times even

when the conversation is short we still

feel the AI is lazy and dumb and that's

because of another more subtle but

impactful limit gbt has difficulties

processing long or or unorganized

content for this long or unorganized

cont contacts Windows gbt may miss

certain requirements especially those

need attention to the details in the

previous example even if the entire

conversation can fit into the context

window it is still not uncommon that you

gbt may give a a codee doing a and c and

forget about B one way to understand

that is we can treat gbt as an intern

that have limited intelligence when it

spends the bring power on somewhere it

has to spend less power somewhere

else and in this example because the

requirements are scattered everywhere

Sean as the green texts gbt needs to

spend actual Intelligence on recognizing

what are the actual requirements from

this long and Massy taxt and that

distract it so it couldn't spend as much

Intelligence on the actual problem

solving and this is just the one side of

the story another potentially even worse

side is the majority of the contact

window is actually bad answers we were

not satisfied with gpt's previous

answers and that's why we further

iterate to add new requests so from the

perspective I probably answer latest

question all the previous answers are

useless or even wrong to some

extent this again distracts GPT it needs

to figure out oh this taxs are actually

wrong where are the problems and how to

fix them as humans we all know that this

is sometimes even harder than getting

something from scratch so overall it is

this scattered around the green prompts

and the long distracting red promps that

degrades gbt's

Intelligence on unfortunately this is

even not the end of the story when we

see AI BEC dumb and lazy what is our

first reaction we chat more give it more

requests and hope this could correct the

problem but now you understand the

context window and its limits you can

see how this is not only not helping but

actually make things

worse because now we have even longer

chat histor to put in the context window

which means it's more likely to get cut

off and more distracting the

requirements are further scattered

around and the bad previous responses

are even longer that makes everything

worse so it's not self correctable in

the case of AI becomes dumb or forgetful

following our first reaction of chatting

more will make it even

worse I'll pause here to review what

just happened we now learn from L

research that the contact window is a

core concept deciding how smart CH GPT

may appear and how effective we could

use it based on this concept what

previously appears frustrating upset and

even weird behaviors now suddenly begin

to make sense when we chat with chat gbt

is nothing more than appending our chat

history to its contacts window and more

chat ends up with a Messier contact

window which will result in a dumb or

forgetful or lazy AI our first reaction

is to chat more to correct it but it

simply doesn't work it's not really our

fault

is mostly rooted from how chbt is

designed it's designed to encourage us

to chat and that inevitably leads to

this

situation but does that mean that we

have to live with it not necessarily

before touching the actual solution

let's detour a little bit to learning to

drive for a moment when we learn about

driving in many cases our first reaction

is actually wrong for example when the

tire blows humans first reaction is to

hit the brake

but it's wrong the best practice is

actually to live it alone and gradually

decelerate the question is how can we

know what is the best practice and how

can we really do that in real

life the answer is simple through

training training tells you how a

technology Works internally tells you

what's the best to practice and why and

helps you practice until make it a habit

until you remember it in your muscle

memory and it also applies to chbt to

properly use chbt we need training too

we need training to correct our firstly

reaction and use good practice to

replace

it and for our specific problem the

solution based on our previous

understanding is we need to manage our

contact window

intentionally instead of blindly

accepting trb suboptimal design we

should proactively think about what our

contact window should look like is it in

the best shape for G to work well if we

follow this mindset it will be easy to

come up with a much more effective

prompt or context window for example

simply put all of the requirements in

one paragraph with nothing else write a

Python program to do a b and c I can I

cannot use

npy but how do we do that does that mean

that we need to start a new conversation

every time when I need to chat that's

not stupid fortunately we have a hidden

feature in the chat gbd UI helps us to

do this contact window

management and that is this small pencil

button below our question it means edit

we can click here to edit the current

prompt and there will be a button say

save and submit clicking it will change

this prompt in the context window rather

than append it to the context window

let's take a look at example here a side

note is we are using gbd 3.5 here

because it has a smaller contact window

and it's easier to trigger those

behaviors gbd4 has the same issue and

benefits from the same trick it's just

not that friendly for this 30 minute

long lining

course come back to our example it's

following the previous example of

copying files from a computer to another

here we further ask chbt oh I cannot

upload the files to the cloud storage

what are my options chbt gives a very

short answer without any detailed

instructions but if we use the addit

trick not how we change the prompt here

we simply copy paste the second prompt

after the first prompt making it a

complete request oh I want to copy files

from computer to another and I cannot

upload the files to the cloud then

suddenly Char becomes more diligent and

intelligent it gives details and

specific and organized answers this is

very different behavior from before and

note here we still use the same model

even the prompt is the same

what was changed is the content of

contact window we intentionally make it

precise and brief and this is the core

of context window

management we can continue the

conversation back to the chat style if

we further ask chbt following the

previous conversations I'm copying files

from a Mac to a PC what shall I do then

chbt gives a bunch of solutions the

third of them is clock storage and

actually we literally just said I cannot

upload files to the cloud in the

immediate previous round stupid right

how to fix it you tell me contact window

management instead of relying on chbt to

append everything and construct its own

contact window we constructed by

ourselves simply append I'm copying the

files from a Mac to a PC to our previous

prompt and that's it gbd suddenly become

smart just like a different Ai No cloud

storage anymore

so let's reflect on this example why did

it work what is the internal mechanism

so that I can use it somewhere

else in the chat style we have the asks

or requirements scattered everywhere and

GPT needs to spend its intelligence on

recognizing and understanding the

requirements but in the added style the

context is well organized with claim

statements on what needs to be done so

GPT could save the intelligence to

really focus on solving the problem in

the chat style Cha's imperfect or

incorrect answer becomes a major part of

the prompt and that's both a waste and

sometimes a distraction of the context

window on the contrary this is not

included in the edit style and we have

efficient use of the context window

meanwhile thanks to the editing feature

we still keep the same ease of

use actually there's the bonus benefit

of the editing style that is the

reusability when we write prompt to help

chbt accomplish some task The Prompt is

not only to explain what we expect from

it but more importantly to correct its

undesired behaviors for example due to

some reason we cannot use numpy in the

company's computer but trb likes to

solve the problem using this Library so

we need to include this requirement in

the prompt however when we use the chat

style it's nearly impossible for us to

use the prompt because such kind of

Correction of unexpected behaviors are

scattered all around the place across

many conversations but for the addit

style use we always have the latest and

greatest The Prompt at hand summarizing

all the requirements and Corrections

ready to use this is especially helpful

in AI assisted programming which we will

cover in the full

course and what we just experienced is

not only a journey to effective use of

chbt but also a great example of

training before training we Face a lazy

forgetful and dumb AI rely on our first

reaction to use it and blame open AI for

when we see unexpected

behaviors after the training we

understood how it works internally we

made made sense of all those behaviors

and learned about the best practices

which is addit not chat the training

lets us know the best way to use chbt is

actually to not chat and in the full

course we will also help you to

exercises to form a habit to make it a

muscle

memory overall the general principle is

for more complicated tools you need more

training it applied to bikes cars planes

and AIS and you need people who knows

the ins and outs and people who know how

to teach to train

you in the full course we will also go

over different components of chbt and

have a comprehensive overview on how to

prepare yourself in terms of mindset

knowledge and practical tricks like

addit not chat we already had the first

two cohorts sold out and the current

cohort will begin from July

22nd all right this is it for linning

course hope you enjoy this and I'm happy

to take any questions let me go over the

chat window to figure out what are the

questions about uh Google so the

question is didn't Google recently void

this issue with the infinit window model

uh yes in the recent I think that's in

last a few days Google proposed this um

but it's my understanding is still in

the pretty research phase after it it

gets into production hopefully we will

have less burden in manually managing

all this context window but before that

this is a pretty easy easy to use and

effective

trick uh and I saw that some of the

questions are already answered and Eugen

is also online to help answer the

questions and the question could you

point us to some resources where we can

dig more into the foundation of L

understand more critical Concepts like

concept Windows this is a pretty new

field and I will say most of resources

online is pretty not unsystematic and uh

at least for this field the this this

makes us painful and we like to have a

systematical overview of what was

important from our perspective so to

direct answer Nick your question um the

best place probably is our our course

but if we see some other resources they

are not a very systematic one on top of

my mind um if I see in the future

probably we can post it somewhere um

such as in the community of our course

what is the target audience of this

course what can we get out of this

course in the end if we are uh talking

about the main course the target

audience of this course is uh the

Professionals in the IT industry by that

I mean we need to have some not basic

knowledge of python such as we know how

to you need to know how to run python

programs you need to know how to read

simple python programs and install

python libraries you don't need to be a

professional developer um for example

PMS TPMS engineering managers uh

analysts the or the target audience of

this course um but we do require some

basic python knowledge so if uh you're

out of the it Prof industry that might

not be the best course for

you and what we can get out of this

course in the end is the general idea is

Gen gen is a pretty effective tool to

boost our productivity but it's actually

there are a lot of petful and nuances as

we just demonstrated in this process if

we understand better from the foundation

and know about the best practices this

will help us a lot in boosting our

productivity my personal experience is

Boost like two times to five times of my

productivity and we' like to also share

this with our the course audience that

is the target outcome of this

course so on us sandbox versus chat

interface that's a great question I I

actually I think using sandbox is a

pretty good way of using uh the gbt API

or chat gbt uh some background is

sandbox is a tool provided by openai

it's developer oriented instead of

showing you a chat interface it in

directly invokes the underlying GPT API

so it's a little bit different from the

chat interface in a few ways

uh difference one is chpt is a product

built on top of GPT API so it has quite

some additional prompts and limits and

features but the sandbox doesn't have

that which means you may not have the

alignment or Safeguard of chbt or other

limits of char BT so you may be able to

get into the dark side of GPT which

means it may not be that safe and the

second is second difference is there is

also some limit POS on the CH on the

chbt for example gbd4 has a contact

window of

128k but in chbt you cannot use that

long before that you will be cut by Char

saying that oh your message is too long

but in the sendbox you are able to use

that the third difference is the uh

maintenance of chat history in chbt web

interface chbt will maintain the context

window as well as the chat history for

you but in the sendbox a little bit more

manual process

which is good thing and from the

perspective of of this course because it

forces you to manually manage the

context window and the fourth uh

potential difference is the price charb

is a subscription based $20 per month

but sendbox is uh per

token so overall it's a pretty

interesting idea to use sendbox I if for

anyone having haven't tried that before

I encourage you to try that it's quite

unique and fun experience and that might

be the right solution to to the context

meal

management do we have an agenda of this

course yes uh in terms of cohort dates

we have that in the latest slide and

feel free to also scan the QR code so

that you get to uh the cord homepage and

know more about the course such as the

uh the the uh syllabus the expected

outcome target audience we have every

everywhere we have everything

there s on using iterations of a highly

structured prompt by edit and or having

start by asking questions to improve

context of prompt then incorporating

into

prompt um that's more about prompt

engineering that's my understanding of

this question um and we do have some

tricks around that but that may be out

of the scope of this context window but

overall yes having the prompt formulated

in a more structured way is definitely

helpful for making trbt smarter as we

mentioned before tra is like it's pretty

much like an intern actually I really

like this analogy it's it's energetic it

doesn't know what is tiest and it can

work 247 but it needs some handh holding

and this kind of PR engineering is a

great example of handholding you cannot

assign a very complicated task to an

intern and expect him or her to

accomplish the task perfectly you have

to do some handholding uh and

potentially even babysitting and prompt

engineering and contact window

management are very good examples of

star

Point um and what might be a more

beginner noice oriented geni

course that really depends on your

background and the intention of what do

you want want to learn and my suggestion

is actually if you want even more

beginner or novice um course probably of

course is not the best option directly

go to the the chat.com or other similar

products begin using that and begin to

feel the capability envelope of geni is

probably the best um thing to do to to

begin with because this will give you a

feeling and

then you also give you some questions

with the questions it will be easier for

you to figure out oh what I want from

this a course like that what are the

pinpoints what can the AI bring to me

what I I think getting those questions

might be more important than figing out

the answers so to directly answer a

question probably the best course is

sign up a account using chbd from today

it seems that the price of course

increases per cohort will that be a

trend um it sounds like a you already

answered

that what are your thoughts on the

content of the course potentially

becoming outdated in mod models iterate

for example context window and

information infinite window model that's

that's a great question uh sounds like a

e already addressed that but generally

the idea are two folds the first is the

content of course can be be basically

divided into two things one is the

underlying research and another is based

on those research that derve the tricks

and skills um for a lot of fundamental

things they will not change in a long

time for example the initial com initial

gbt model has been proposed like six

years but it's the basic PR principle

Still Remains the Same and another side

is we do see this field is growing very

fast that's also reason why ourselves

are Inus as in Inus as of this field and

we keep up with the field every day um

we try to figure out what are the most

important tricks and principles and

distill them into the course to make it

up to

dat um due to the limit of time we will

take the last question here um Yen have

you played around cloud. a pick out of

curiosity yes I played with it and it's

it's quite interesting uh in terms of

two aspects the first is I found that

the difficulty of managing my contact

window is much less for cloud V3

especially Opus more more um

specifically for a lot of prompts and

contact Windows gbt may be lazy so we

need to do more tricks to make to like

massage those prompts and contact

Windows to make it work but for cloud V3

we don't need to do that tricks from

this perspective we can see a trend that

people also realize the problem with

contact window and Make it try to make

it more user friendly and from another

perspective I also feel gbts 4 is still

a little bit smarter than Cloud a floud

V3 but that may be just my feeling based

on limited

tests so due to the limit of time uh

that's it for the Q&A and really

appreciate everyone's time and interest

for attending this course we look

forward to see you in the actual course

thanks a lot bye