If you spend any serious time with advanced AI
chatbots, you probably know the biggest headache.
It's context drift. You spend ages detailing
a marketing brief, defining your audience, setting
the tone. Perfect. Then you ask a quick question
about, say, weekend plans. And suddenly, the
AI is suggesting beach resorts right in the middle
of your formal client memo. You find yourself
constantly repeating things, restarting chats.
The AI forgets what you were doing just five
minutes ago. Well, it's frustrating. Kills efficiency.
It really does, yeah. And you feel like you're
almost fighting the tool, instead of it helping
you. But there is actually a surprisingly powerful
solution, one that a lot of users, even experienced
ones, seem to overlook completely. It's called
Chat GPT Projects. Prolix, OK. Yeah, and this
is, I think, genuinely the most effective way
to get that consistency and generate really high
quality results over the long term. And the great
news, this is now available for everyone. Even
if you're on a free account, you can use this.
It is. So our mission today, it's pretty straightforward.
We need to unpack what projects actually are,
how they're fundamentally different from, say,
custom GPTs, which people might know, and maybe
most importantly, nail down the three essential
parts you absolutely need to set up to create
like a truly focused expert AI assistant just
for your specific work. OK, that sounds exactly
like what's needed. Let's dig into the mechanics
then. How do they work? Let's do it. So the way
I've started thinking about them is like private
work rooms inside chat GPT, almost like custom
-built spaces. They don't seem designed for just
quick... one -off questions, you know, more for
ongoing long -term work where the knowledge needs
to build up. That's a perfect analogy. Yeah,
a private workroom. Or think of them as dedicated
AI assistants, each one totally focused on a
single domain. Maybe one handles all your marketing
copy, another manages your coding questions.
And the key feature, the absolute core of it,
is dedicated memory. Dedicated memory. Exactly.
They remember everything within that project.
All the past chats, every single file you've
uploaded, all the instructions you've given it.
It's all stored. neatly right there in that one
space. And because that knowledge base is isolated,
the AI stays laser focused, always on that specific
topic. OK, so if these work rooms are meant to
keep things separate, how do they actually stop
that confusion? Like, how does it prevent my
random search for a trip to Dalat from bleeding
into my serious marketing strategy project? What's
the barrier? Well, projects keep their knowledge
totally separate. So you get focused, consistent
results without that random contamination. Got
it. That makes sense. Now, a lot of people listening
might have used custom GPTs before to personalize
things. So it's really important to understand
the difference here. If a project is that workspace,
that workroom, a custom GPT is more like a specialized
tool. It's built for one specific repeated action.
Like maybe it only generates email subject lines
or it checks grammar in a very specific style.
You kind of set it up once for that function,
and it doesn't really change much after that.
Right. OK, so GPTs are tools, but projects. They
sound more like living documents or evolving
workspaces. They grow and update as you use them
more. You're not just doing one task in the project,
you're managing a whole area of work. It's an
ongoing thing. Exactly. Living documents is a
great way to put it. And the core difference
really comes down to where the intelligence comes
from. A project's knowledge is based on your
initial instructions, yes, and the files you
upload, but also the entire history of every
single chat within that specific project. custom
GPTs. They pretty much just rely on how you set
them up initially. That's their knowledge base.
So the chat history itself becomes part of the
project's brain, essentially. Precisely. Think
about, say, managing a YouTube channel with a
project. You go back to it every week, right?
Brainstorming ideas, drafting scripts, because
it's building that full history of every interaction,
every decision, every script draft, the suggestions
it gives you three months down the line. They'll
be incredibly accurate because it knows your
channel's whole journey, your style, your topics,
what worked before. So if projects gain knowledge
from the conversation history, does that mean
they actually get smarter over time, more tuned
in to my specific needs? Yes, they learn from
every conversation, getting smarter and more
precise about your task over time. Wow, OK. But
that learning, that growth, it totally depends
on keeping the data clean, isolated. Which brings
us to, honestly, the first critical thing you
need to do before anything else. The memory setting.
The memory setting. Okay, why is one setting
so crucial right at the start? Because of that
context contamination we talked about, it's a
default setting issue, really. By default, when
you create a new project, it can actually access
memories from your other chats, your normal,
everyday, random chat GPT conversations. Oh,
really? Yeah. So it might remember you were looking
up, I don't know, Italian seasoning recipes last
Tuesday, and now it's trying to stick a weird
spice metaphor into your serious corporate financial
report. That's bad news for Focus. Definitely
don't want that. No. So immediately, the first
thing you do... Change the setting to Project
can only access its own memories. Full stop.
That stops it pulling in random stuff like your
recipes or that Daylat trip plan. It keeps the
project's brain focused only on the project job.
And honestly, this is a bit of a vulnerability
moment here, but I still wrestle with prompt
drift myself sometimes, even after using these
tools for ages. This setting. It's like your
main defense. It stops those weird, irrelevant
mistakes before they even happen. It ensures
your specialized AI stays specialized. So just
switching that memory setting to project only,
does that significantly cut down the chances
of the AI making those off -topic, irrelevant
mistakes? Absolutely. Isolating the memory ensures
the AI stays laser -focused on its job. No distraction.
OK, crucial step number one. Isolate the memory.
Got it. mid -roll sponsor, read placeholder.
All right, we locked down the memory. Now let's
talk about building the project itself. You mentioned
three pillars for setup. What's the first one?
The first pillar is custom instructions. And
this is so much more than just telling it to
be polite. This is basically where you write
the job description for your AI assistant. You're
setting the rules of engagement, eliminating
ambiguity. You define its role, its primary goal,
the format you want answers in, and the overall
tone. Most people probably just put in something
vague about tone, like be friendly. Yeah. But
you're saying getting really specific here is
key. That's what makes it a real assistant. Exactly.
Specificity is power here. Let's say you're a
writer planning social media. Define the goal
clearly. Help me plan content for a Facebook
fan page about pets. Simple. Define the format.
Always answer using bullet points. Do not write
paragraphs longer than five sentences. Super
clear. And the tone. Friendly, fun, enthusiastic,
clearly loves animals. And you can even add special
requests. Like if my request isn't clear enough,
please ask me clarifying questions before answering.
That saves time. Right. Prevents bad outputs.
Totally. And the poodle example really shows
this power. You could define the role as content
manager assistant for a poodle enthusiast fan
page. Then you specify the format. Each post's
idea needs a catchy title, three to four bullet
points explaining the idea, a clear call to action,
you know, a CTA, telling people what to do next,
and always suggest a relevant picture or video
type. That's incredibly detailed. But that detail
is what gives you consistent high -quality output
every single time. No guesswork for the AI. Okay,
custom instructions is Pillar 1, what's Pillar
2? Pillar 2 is the knowledge base. This is the
project's brain, basically, where you feed it
the specific data it needs. You upload your files
here, PDFs, maybe text documents, transcripts,
internal guides, whatever is relevant. But the
key here is quality knowledge, not just dumping
everything in. Relevance is crucial. And there
are limits on files, right? Yeah, there are.
It varies by plan. Free users get, I think, five
files per project right now. Plus in education
get 25. Pro and enterprise get up to 40. But
honestly, Even five is plenty if they're the
right five files. Five super relevant, high quality
documents beat 40 random, vaguely related ones
every single time. So be selective. Don't just
upload your whole hard drive. Exactly. Quality
over quantity. Focus. That limit kind of encourages
you to think strategically about what knowledge
is truly essential. Make sense. Okay. Instructions,
knowledge base. What's the third pillar? The
third pillar is tools. Projects can optionally
access special tools to enhance their capabilities.
To of like what? Well, there's Canvas, which
is really cool. It lets the AI help you create
documents, presentations, even quizzes right
inside the chat interface. There's deep research,
which can scan hundreds of websites to gather
in -depth information on a topic. Very powerful.
Then you have image generation for creating visuals
and the standard web search for pulling in the
latest real - time info. So should we just turn
all those on? No, definitely not. Here's a critical
tip. Don't turn these tools on by default. Only
enable them when you specifically need that capability
for a particular task. Why? Because you want
the project to primarily rely on the custom instructions
and the knowledge base you carefully curated.
That maintains focus. Use the tool selectively
as needed. Given those file limits, especially
for free users, is the best strategy really just
picking the absolute most vital documents for
that knowledge base? Absolutely. Quality, focus,
and relevance are the deciding factors for useful
AI output. Five great files beat 40 mediocre
ones. Got it. Let's make this concrete. Can you
walk us through setting up a project for a real
world task? Sure. Let's take learning English.
We'll build an English learning plan assistant.
So, for custom instructions, the goal might be,
help me improve my English listening and speaking
skills. Format. Provide weekly plans in a simple,
clear table. Tone. Encouraging and patient. And
a crucial special request. Before creating the
new week's plan, always ask me if I completed
last week's goals and what challenges I faced.
That builds in accountability. Nice touch. Okay,
what about the knowledge base? What files go
in there? We need actionable data for the AI,
so maybe upload. A list of 500 common English
vocabulary words the learner wants to master.
A short document listing the learner's most common
grammar mistakes may be based on feedback from
a teacher, a transcript from an English podcast
they like, to analyze style and phrasing. And
maybe there's score report from a recent English
test to show baseline weaknesses. OK, specific
relevant data. Exactly. And here's that smart
tip you mentioned earlier, a really useful technique.
Before you even upload files to the project,
go into a normal, separate chat GPT chat, turn
on the deep research tool there, ask it something
like, generate a detailed report on the most
effective methods for an intermediate learner
to improve English listening skills. Ah. Let
it run. Do its thing. Generate that report. Then
download that report as a PDF or text file. Then
you upload that curated report into your English
learning project's knowledge base. Ah, so you're
using the AI's research capability to create
high -quality knowledge for your project's brain.
Precisely. You're feeding it expert -level information
sourced from the web, but curated and focused.
Okay, so instructions set, knowledge uploaded.
What happens next? Then you just ask for your
plan. Generate my English learning plan for this
week. The projects looks at your goal, your common
mistakes file, the vocabulary list, that research
report, and it creates a specific personalized
table like day one, watch this specific five
minute Ted Ed video on topic X, summarize it
aloud in three sentences, review vocabulary words
10, 15 from the list focusing on pronunciation
because it knows that's a weakness from your
files. The level of personalization sounds incredible.
It really is because it's all based on your specific
data and goals stored right there. Okay, let's
try another one. Marketing. Say we have a new
product, a smart thermos. Create example. So
knowledge base first. We upload maybe a PDF with
all the product features, keeps drinks hot or
cold for 24 hours, LED temperature display, uses
316 stainless steel, and maybe another file defining
our target customer. Office workers, gym goers,
maybe age 22 to 35. Perfect. Clear product info,
clear audience info. Now, this is where a tool
like Canvas could be really useful. You could
ask the project right in the chat. Create an
introduction flyer for my new smart thermos using
the Canvas tool. Use the features from the uploaded
PDF. And it will build a draft flyer. Yeah. It'll
pull those features, the 24 -hour stat, the LED
screen, directly from the file you uploaded.
It ensures the marketing content is accurate
and based on the facts you provided. You can
edit it right there. Yep. You can tweak the layout,
change text, then share it instantly. get a public
link, or download it as a PDF, all within that
dedicated project workspace. That's efficient.
And what about images? Easy. In the same project
chat, you could then say, now, generate an Instagram
ad image for this thermos, include the text,
keep the taste all day long, and it'll create
image options for you. Wow, OK. Instructions,
knowledge, tools, all working together. Exactly.
Whoa, just thinking about this. Imagine scaling
this, like an entire company using projects.
Every team, every department having these dedicated
expert AI assistants all adhering to the company's
internal standards using only approved knowledge
bases. The level of consistency, the domain specificity
across an entire enterprise. That's genuinely
transformative potential right there. That is
a big thought. OK, so these projects are powerful
because they're focused on their internal knowledge.
But do they have to be completely cut off from
the outside world forever? What about new trends?
Good question. No, they don't have to be totally
isolated You can smartly combine that internal
knowledge with fresh online information when
needed So you're in your smart thermos marketing
project, right? You've got your features PDF
your customer profile, but you need the latest
trends What's hot right now in that market? You
don't leave the project. You just click that
little plus icon in the chat input box Temporarily
enable the web search tool just for that one
query Exactly. Then you ask, what are the latest
marketing trends for stainless steel lifestyle
products relevant to my target audience this
year? Chat GPT goes out, searches the web, and
comes back with current insights. Maybe it says,
focus on sustainability narratives or collaborate
with KOCs. KOCs. Oh, right. Key opinion consumers
or key opinion leaders. Basically, real customers
or influential users who genuinely use and love
the product rather than just paid celebs. Their
recommendations carry a lot of weight. Got it.
So the project finds these new trends. And that
new information, those trends, that insight about
KOCs, it gets added automatically right into
that project's memory, its conversation history.
So you're efficiently updating your project's
knowledge with fresh, relevant data from the
outside world. But you did it strategically.
Then you just turn web search off again, and
the focus snaps back to your core internal knowledge
base. Best of both worlds. That's a smart way
to keep it current without losing focus. What
about other, maybe less obvious features? Any
efficiency boosters people miss? Yeah, a couple
of good ones. If you have a really great conversation
going in your regular chat, GPT chat, and you
suddenly realize, wow, this should be a project,
you can actually move that entire existing chat
conversation with all its context directly into
one of your projects, or start a new project
from that. Oh, that's handy. Instead of copy
pasting. Exactly. Keeps the history intact. Saves
a ton of time if you realize mid conversation
that it's becoming a long term thing. And another
one, especially for the mobile app users, voice
input. Having a spoken back and forth conversation
with your dedicated project assistant using voice,
it feels incredibly natural. Almost like talking
to a real specialist assistant. Really good for
brainstorming on the go. OK, those are useful
tips. Now, on the flip side. What are the big
mistakes people make when they first start using
projects? Where do they usually go wrong? Good
question. There are a few common pitfalls. Number
one, trying to cram everything into a single
project. Don't create one massive, my whole life
project. Keep them hyper -focused. One for marketing,
one for coding help, one for learning Spanish.
Specific domains. So specialization is key. Absolutely.
Mistake number two. Rushing through or completely
ignoring the custom instructions set up, people
skip this, just give it a name, and wonder why
it's not very helpful. Those detailed instructions
are vital. They tell the AI how to be useful
for you. Right. The job description. Exactly.
Mistake three. The opposite of being selective
with the knowledge base, just uploading tons
of unrelated files. Garbage in, garbage out,
basically. Quality and relevance matter way more
than volume. Right. Focus, instructions, quality
knowledge. What else? The big one we already
covered. Forgetting to change that memory setting
to project can only access its own memories.
That causes instant chaos and undermines the
whole point. Isolate the memory. Got it. And
maybe one last thing. Starting a brand new chat
inside the project every time you use it. The
real power comes from building on the history.
Continue the existing conversation thread so
it learns and remembers context over time. Don't
keep starting from scratch within the project.
Okay, that makes sense. Build on the history.
So let's try to wrap this up. The big idea here
seems really clear now. Projects are essentially
about creating expert AI assistants. defined
by those three key things, your custom instructions,
the specific knowledge base you feed it, and
the cumulative conversation history that builds
up over time. That's it. Instructions, knowledge,
history. The three pillars. And the main benefit
is getting consistent, reliable, high -quality
results that are specifically tailored to your
long -term tasks or areas of work. Exactly. So
just to summarize the when to use what's question,
use projects for anything that's a repeated task,
requires long -term context, or needs a special
dedicated knowledge base that you provide and
control. Your goal with projects is building
that expert AI assistant that grows and learns
alongside you for a specific domain. regular
chats are still great use them for quick one
-off questions things that don't need history
or special knowledge you know what's the capital
of Australia or convert these measurements quick
in quick out got it projects for depth and consistency
regular chat for quick hits okay here's a final
thought something that really struck me about
this memory system the truly mind -bending power
maybe months down the line is the project's ability
to gently correct you based on some detail from
a conversation you've completely forgotten. Like
take that English learning system we talked about.
Maybe you're drafting something and you use the
word effect when it should be effect. The project
could potentially say, hang on, remember back
in week three, we discussed the difference between
affect and effect. Based on our conversation
then, you determined effect is the noun you need
here. Wow. That correction didn't come from the
grammar file you uploaded initially. It came
purely from the memory of your past conversations
within that project. Well, that's a true assistant.
That is powerful. It's not just recalling files.
It's recalling the learning process itself. OK,
so your action plan, if you're listening, it's
simple. Don't overthink it. Just create one project
today. Call it something simple and actionable,
like my blog post ideas or meeting notes analysis.
Set just three basic custom instructions. Maybe
it's role, main goal, and preferred output format.
Add only one or two key files to the knowledge
base, maybe your brand style guide or notes from
a recent important meeting, and then just commit
to using that project consistently for one week.
For anything related to that topic, see how that
isolated memory, that growing history, starts
to actually serve you, how the outputs get better,
more tailored. Great starting point. Apply this
knowledge. Set up that first some project today.
Don't wait. See for yourself how quickly it can
improve your consistency and the quality of what
you get back from the AI. It really can make
a difference. Out to your own music.