Have you ever found yourself kind of caught in
that loop with ChatGPT? You know, where you're
starting a brand new conversation every single
time. And all that valuable context you just
built up just sort of vanishes. Oh, tell me about
it. It's a bit like Groundhog Day for your digital
workflow, isn't it? Absolutely. You're constantly
re -explaining the background, re -uploading
the same files, repeating preferences over and
over again. It's a genuine productivity drain.
It really is. It's frustrating. But what if there
was a smarter way, a way to actually organize
your AI work and consistently get really high
-quality results back? Well, today, we're taking
a deep dive into exactly that. We're going to
unpack ChatGPT projects. We'll look at what they
are, why they're actually pretty powerful, and
how you can set them up to hopefully transform
your productivity. Yeah, let's do it. We can
walk through some real examples, too, step by
step. Perfect. So let's unpack this. So thinking
back, most of us, when we first started with
ChatGPT, we probably used it like a simple Q
&A tool, right? Yeah, basically. You type a question,
you get an answer, maybe a follow -up. Exactly.
But the frustration really kicks in when you
move to the next task, even if it's closely related.
You feel like you have to start all over. You
do. You hit that reset button. You find yourself
re -explaining the whole context, your business,
the project, the audience you're talking to.
Re -uploading documents. Re -uploading, yeah.
Reiterating the formatting you want. And the
AI just starts completely fresh. Zero memory.
of what you just did. That starting from scratch
problem, it's such a real bottleneck. We saw
that a lot in the user feedback people shared.
It wastes so much time and maybe even more importantly,
it leads to really inconsistent results. Right.
I mean, think about it. If the AI doesn't remember
your brand specific tone or your clients unique
needs, how can it possibly give you the right
output every time? Exactly. It can't. And this
is precisely the problem that chat GPT projects
are trying to solve. They're not just, you know,
glorified folders for storage. That's a common
misconception, isn't it? Yeah, it really is.
Instead think of them more like dedicated intelligent
workspaces. Okay. Digital environments built
specifically for that ongoing multi -step work
where context is absolutely critical. So whether
you're managing say content creation for a client
or a big marketing campaign or even writing a
book. Yeah, anything complex or ongoing. Projects
aimed to keep everything organized and crucially,
contextually aware. That's the key. Every new
chat you start inside a project, it's already
preloaded. How so? It gets immediate access to
all the relevant past conversations within that
project, all the files you've uploaded, like
brand guidelines, research docs, customer data,
and maybe most importantly, your custom instructions,
which means you literally never start from zero
again. every conversation builds on that foundation.
Leading to better, more consistent results, presumably.
Way more consistent, yeah. And just a quick practical
note here, based on the articles we looked at,
projects are currently only for chat GPT plus
and pro users. Right, so if you don't see projects
in your sidebar, that's probably why. Probably.
So, okay, beyond just saving a few clicks here
and there, what's the real, like, core difference
this brings to how we use AI day to day? It's
an intelligent workspace, no more starting over.
Got it. Let's talk practical setup then, based
on the guide. Step one seems simple enough. Just
creating the project. Yeah, pretty straightforward.
On the left side of your chat GPT screen, you'll
see a new project button, usually with a little
plus icon. Click that and it kicks things off.
Yeah. Now, this next bit seems surprisingly important,
but maybe easy to overlook, naming it. Oh, definitely.
Give it a clear descriptive name. Don't just
call it project one. Right. So like the example,
if you're Momentum Digital working for TechUp,
maybe Momentum Digital Marketing for Client TechUp.
Exactly. Something where you can just glance
at it and know exactly what's inside. Then just
click Create Project. OK. Project created. It
pops up in the sidebar. And then you mentioned
a visual tip. Yeah. A simple but helpful one.
You can change the folder color. Just click the
little folder icon at the top of the project
page. And you can color code things. Maybe blue
for client work. green for internal stuff, red
for urgent tasks, whatever system works for you.
Nice visual cue. And then step three is choosing
the AI model. Right. Projects let you use different
models within the same workspace. You've got
GPT -4 .0 for the complex, creative stuff, GPT
-4 .0 mini for maybe quicker, simpler things,
and GPT -3 .5 for really basic stuff. But for
most serious business tasks, like marketing.
Yeah, for that nuanced understanding and quality,
GPT -4 was probably still your best bet. That's
what we generally recommend sticking with for
the core work in a project. OK, makes sense.
So how does this initial setup, these first few
steps, really benefit our overall workflow in
the long run? Visual cues and model choice streamline
your process. OK, now we get to what the guides
and articles. really highlight as the core components.
The real magic, as some put it. Yeah, this is
what turns it from just a folder into that intelligent
workspace we talk about. And that's files and
custom instructions. Exactly. The two pillars.
Let's start with files. Uploading relevant files.
You said these become like a dedicated knowledge
base. Precisely. It's like giving ChatGPT its
own private library just for this project. Only
the relevant info. So for that marketing project
example, what kind of files are we talking about?
OK, so for our momentum digital tech up example,
you definitely want client documents, things
like detailed target audience personas, user
pain point analysis, market research, competitor
analysis. Absolutely. Then you layer on your
brand materials, the detailed guidelines, tone
of voice docs, maybe even examples of past successful
campaigns. Got it. And don't forget product or
service info, feature descriptions, pricing,
technical docs maybe so the AI really gets what
you're selling. And social proof, testimonials,
case studies. Super valuable, yeah. Ads credibility.
And uploading is easy. Just click add files,
grab your documents. You can do multiple at once.
Build up that knowledge base. OK, that's the
files foundation. Now, custom instructions. You
said this is where the magic really happens.
Yeah, I think so. This is like giving ChatGPT
its detailed brief, almost its personality for
this specific project. So what makes for great
custom instructions? It can't just be, be helpful.
No, definitely not. You need specifics. First,
assign it a clear role. Tell it who it should
be. expert consultant, a creative copywriter.
OK. Then specify the response format you want.
Define the tone and style really clearly. Include
any unique guidelines just for this project.
Reference points, too, like specific authors
or styles to emulate. Yeah, exactly. So for our
TechUp B2B example, the instructions might be
something like, core role, you are a growth marketing
consultant for B2B tech startups. OK, very specific.
Focusing on attracting technical audiences for
style. Inspired by Seth Godin, professional,
tech savvy, focused on benefits, not just features.
Nice format. Structured recommendations, use
bullet points, headings, always suggest a title
and a call to action for content. And the business
context. Client is tech up, saws for developers.
Target, devs, tech leads, CTOs, goal, increase
trial signups, avoid cliche marketing jargon,
be authentic. That's pretty detailed. It needs
to be. You know, I still wrestle with prompt
drift myself sometimes. Yeah, where it kind of
loses focus. Exactly. It starts to wander off
from the original instructions over a long chat.
Getting these custom instructions really dialed
in is, it's an art, beat, but when you nail it,
wow, it makes such a difference. It really sounds
like these two things, the file providing the
knowledge, the instructions providing the personality
and direction are the absolute heart of it. Why
are they so fundamentally critical together?
They provide the core brain and personality for
your AI. Okay, let's make this concrete. A real
world use case, like the ones in the studies,
say creating an outline and intro for a technical
blog post. Alright. For our TechUp example, maybe
the task is detailed outline and intro for automating
your API testing workflow with TechUp's new feature.
So instead of just that generic prompt, you'd
leverage the project context. Right. Specify
the target devs, QA engineers. Address their
specific pain points with manual testing. Introduce
the new feature as the solution. Right. Build
credibility. Maintain that tech savvy tone from
the instructions. And maybe specify the format
detailed outline with HUSH3s and like a 150 -200
word. And with a well set up project, the output
should be pretty impressive. You'd get that detailed
outline, H2s like the problem with manual API
testing, H3s like time drain, scaling issues,
and then a solid intro. Something like, if you're
a developer, you know the pain. Hours on repetitive
API tests. It's a bottleneck. What if you could
automate it? TechUp's new auto -test feature
makes that real, you know, something engaging
like that. And it works well because the AI has
all that context from the start. Exactly. It
knows the audience, the product, the desired
tone from the files and instructions, so it maintains
consistency, delivers professional quality, it
has guardrails. That's pretty efficient. OK,
now let's take that blog post and promote it.
OK. Use case hashtag two, a LinkedIn post series.
Good one. Now, this is a related task, but different
social media, not long -form content. So key
step. Start a new chat within the same project,
right? Crucial step. Yep. Keeps things organized.
But you still get all that lovely context. Tech
up, the product, the audience, the tone. It's
all still there. Okay, so the prompt for the
LinkedIn series might detail, say, a three -post
strategy. Yeah, like, post one is a hook about
the pain points. Post 2 introduces the blog feature
with benefits and a CTA. Post 3 offers social
proof or a future vision. Makes sense. And it
would generate those posts. Yep. Like Post 1
might be. Still manually testing APIs, flaky
tests, regressions. It's a productivity killer.
There is a better way. TechUp's working on something.
Stay tuned. You know, with tailored hashtags,
too. And the power here is that consistency.
Absolutely. The project continuity. The whole
series sounds like it came from the same brand
as the blog post. Unified voice, coherent message,
totally brand aligned with TechUp's goals in
the project. Wow. Think about scaling that unified
voice. Whoa. Imagine scaling that across hundreds
of campaigns without re -briefing every time.
That's the real power. So it's not just faster
content. How does this process really save us
significant time and, frankly, mental energy?
AI understands your brand, generating consistent
tailored content faster and smarter. Okay, so
to really get the most out of this, we need to
avoid some common mistakes. Based on what we've
seen in forums and guides, what are the big pitfalls?
I think the biggest one is just treating projects
like basic folders. You know, creating one, but
then not actually using the files and custom
instructions. Right. If you don't feed it that
context, it's basically just an empty box. No
different from a regular chat. Exactly. It defeats
the whole purpose. Another one is uploading too
many irrelevant files. Ah, information overload
for the AI. Yeah, basically. If you just dump
everything in there, it kind of dilutes the context.
The AI struggles to figure out what's actually
important for this specific task. So be selective.
Curate the knowledge base. Definitely. Choose
only the critical, relevant docs for that project's
goal. And we absolutely have to talk about vague,
custom instructions again. That seems like a
major issue. Huge. Just saying, be professional
or write well, that gives the AI almost nothing
to work with. Useless. Pretty much. You need
those hyper -specific details. The role, the
audience, the tone, the business context, like
we talked about, precision is key. Got it. Anything
else? Yeah. One more big one. Trying to do too
many different things in the same chat window?
The kitchen sink approach. Exactly. Don't ask
it to write a blog post, then immediately draft
an email, then do social posts all in one continuous
thread. Why not? Because the context gets modeled.
Each task really benefits from a focused conversation.
So start a new chat within the project for each
distinct task, even if they're related. Keeps
things clean and effective. Right. Otherwise,
it's like trying to bake a cake, fix a car, and
write a novel all in the same kitchen sink. It
just gets messy and nothing turns out quite right.
That kitchen sink analogy definitely sticks.
Yeah. So if there's one big takeaway for listeners
to avoid these traps, what is it? Be present
with context and keep your AI tasks focused.
Okay, one area of confusion that often pops up
is the difference between chat GPT projects and
custom GPTs. Let's try and clarify that. Yeah,
good idea. They sound similar, but serve different
purposes. Think of custom GPTs as specialized
tools. They're built for specific, often repetitive
tasks. They can have a unique personality, they're
easily shareable, and they can even connect to
external things like APIs. More like single purpose
apps and projects. Projects are more about organizing
ongoing evolving work. They're your personal
workspaces. Their main job is storing files,
remembering instructions, and keeping that rich
chat history going over time for a broader initiative.
So give us the when to use which breakdown. OK.
Use projects for managing complex client work,
multi -step content creation, needing a consistent
voice, or any long -term thing where context
builds up. And custom GPTs. Use custom GPTs when
you want to build a specific repeatable tool,
like an expert ad copywriter GPT that always
uses a certain framework, or an AI assistant
for your team that does one specific task often.
They're for narrow specialized capabilities.
Gotcha. But they aren't mutually exclusive not
at all and this is where it gets really powerful.
You can absolutely use them together How would
that work? Well, our agency, Momentum Digital,
could use a project for all the work for the
TechUp client blogs, social strategy, everything.
But they could also have a custom GPT, maybe
called LinkedIn Post Optimizer, that the whole
marketing team uses across all clients, including
TechUp, just to refine those posts. Ah, so the
Cumptive GPT is a tool used within or alongside
the broader project structure. Exactly. It lets
you layer workflows in a really advanced way.
So these two powerful features, projects and
custom GPTs, they can actually work together,
complementing each other. Absolutely. They complement
each other perfectly for advanced workflows.
OK, let's wrap this up. What does this all really
mean for your day -to -day workflow with AI?
What are the big ideas? Well, first, I'd say
setup is everything. Really take that time upfront
to configure your project with detailed instructions
and truly relevant files. That's the foundation,
isn't it? It absolutely is. And that leads to
the second point. Context is king. The more relevant,
thoughtful context you provide, the better and
more precise your results will be. The AI just
gets smarter about your needs. And third? Organization
pays off. A good project structure isn't just
about tidiness. It genuinely saves hours and
massively improves consistency, reduces that
mental load. Always be improving. Your projects
shouldn't be static. Review them, optimize them.
They're living workspaces, right? They should
evolve. This whole feature. It really feels like
a leap. Moving from just basic AI chats to something
much more like sophisticated context -aware collaboration.
The time you invest in really getting the hang
of chat GPT projects today, it seems like it
will genuinely pay off down the line. Maybe the
best advice is just start small. Totally. Start
with one project right now. Upload just a few
key files. Write your first clear set of instructions.
And just see the difference it makes. Exactly.
Experience it. Your productivity and honestly
the quality of what you create. It probably won't
be the same. Yeah. This feels like the point
where you shift from just talking to AI to really,
truly collaborating with it. Reflective, calm.
Yeah. Just consider how much more deeply the
AI can understand you, understand your goals
as they evolve, when you actually give it a dedicated,
persistent home for your work. Two secs silence.
I've got it. Have too many music.