So you've got access to chat GPT -5. Powerful
stuff. But maybe the results still feel a bit
flat sometimes. Yeah, it happens to everyone.
You've got this amazing tool, but the output
can sound generic, kind of uninspired. Right.
Beat. But don't panic. The problem usually isn't
the AI itself. It's more about how we're asking
the questions, how they're communicating. Exactly.
It's like we're giving a jet engine instructions
meant for, I don't know, a lawnmower. We're not
tapping into its full capacity. It's that classic
capacity paradox. advanced tool basic instructions.
So welcome to the deep dive. Today we're going
straight past the basics. We've dug into the
research and pulled out 12 really proven techniques
to shift your chat GPT -5 results from okay to...
well, masterpiece. This is going to be your shortcut
to getting sophisticated results. We've structured
it around three main areas. What are they? OK,
first up, the core prompting mindset, like the
actual words you use to make the AI think harder.
Then second, we'll get into context and using
different inputs, files, pictures, the new branches
feature, building that deep memory. Yeah, the
multimodal stuff. And finally, we hit what we're
calling expert mode, looking at automation with
agent mode, custom personalities, and crucially,
managing its memory. By the end of this, you
should feel genuinely well informed on how to
use this thing at a much higher level. Let's
dive in. All right, segment one, foundational
prompt engineering. Let's unpack that core prompting
mindset first. The research we looked at really
highlights this idea that your prompt sends a
signal. It tells the AI how hard it actually
needs to work on your request. So it's like the
AI internally assesses the prompt and decides,
OK, how much energy should I put into this? Pretty
much. If the prompt seems simple, it defaults
to like a conservation mode. It doesn't go all
out. You need to tell it to override that. Ah,
so the default is kind of lazy mode unless we
specifically push it. Exactly. And that brings
us to tip one, the magic phrases. These are specific
words or sentences you add to your prompt. Things
like? Things like, think step by step or. be
extremely thorough, or maybe take your time.
And even this is critical to get right. So these
phrases are basically like telling a colleague,
hey, this one's important. Don't rush it. Put
some real thought into it. Precisely. It signals
maximum effort required. And the examples show
a clear difference, right? Like drafting a simple
social media post. Yeah, the basic prompt gets
you basic text. But you add those phrases, specify
structure, ask for details, maybe even tell it
to prompt interaction like, tag a friend. And
suddenly the quality jumps way up. It's much
more usable immediately. Okay, so we can tell
it to think harder, but how do we make sure it
actually meets our specific quality standards,
especially for complex tasks? That leads right
into tip two. The self -critique method. This
is where we make the AI grade its own work before
it even shows us anything. Ooh, I like that.
How does it work? You give the AI a quality checklist
before it starts writing. You define what good
looks like up front. OK, so say you're writing
a really important job application email. Right.
Your checklist might demand tone must be professional
but also excited, needs perfect clarity, must
mention specific company details personalization,
and needs a clear call to action like asking
for an interview. Got it. And then you tell the
AI. You tell it to run up to five internal improvement
cycles using that checklist. It basically edits
itself, refining the draft against your criteria
without you seeing the messy in -between steps.
It acts as its own secret editor. That's clever.
It is. You know, I still wrestle with prompt
drift myself sometimes. My intention gets kind
of fuzzy after a few back and forths. Oh, totally.
Happens to the best of us. But this self -critique
idea feels like... Genius, almost, because it
locks in your definition of success right from
the very start. It forces the AI to stick to
the plan. Which connects perfectly to tip three,
something that trips people up all the time.
Never give mixed messages. We do it accidentally,
constantly. The classic contradiction, be brief,
but provide detailed explanations. Right, the
AI gets stuck. It's like telling someone to walk
straight and turn left at the same time. It just
fuzzes out, can't do both well. So the fix is
clarity, prioritization. Exactly. separate the
requests, like first provide brief summaries.
Then maybe I'll ask for detailed explanations
on specific points if I need them. That way it
can focus all its processing power on one clear
task at a time. Makes sense. So tying this together,
since getting that initial quality right seems
so dependent on having clear intent, how much
time should people really invest in refining
that very first instruction? Well, based on this,
defining success clearly upfront using these
methods. It'd probably save you ten times that
amount later, not having to fix vague or confused
outputs. Good point. Clarity upfront prevents
messy cleanup. Okay, moving on from the words
themselves. Segment two gets into building context
and using different kinds of input. Tip four
highlights this big shift toward true multimodal
input. Yeah, this is huge. We're not just limited
to text anymore. We can use pictures, upload
files, even use our voice to give the AI context.
It really changes things, especially for stuff
that has structure or visual elements. Definitely.
Take the CV analysis example from the source
material. You upload your CV, maybe as a PDF,
maybe even an image. OK. And then you prompt
ChatGPT, telling it something like, act as an
expert HR manager with 15 years of experience
and critique the CV. And because it can actually
see the layout, the design, the white space,
it's not just reading the words. Exactly. It
critiques the visual structure alongside the
wording. It gives you actionable feedback on
things you'd normally need a human consultant
for. That's pretty powerful, like getting expert
design feedback instantly. And a pro tip they
mentioned. Upload your version, maybe the bad
one, and also a good example you like. Then ask
it for visual comparison advice. Oh, smart. Side
-by -side critique. Also, quick note on voice.
The little microphone icon. That's usually just
for transcription. But the sound wave icon. That's
the more advanced back and forth conversational
mode, like a real assistant. Good distinction.
Okay, tip five takes us from analysis to creation.
Building simple apps using something called Canvas
mode. Yeah, this is cool. It's basically designing
useful little custom tools without needing to
write any code. Like the example they gave, a
weekly meal planner and shopping list generator.
Right. You tell it what you want, specify the
design, use light colors, make two sections,
and the functionality, I need seven boxes for
meals, a shopping list I can check off, and crucially,
a button labeled generate my week that automatically
fills in the ingredients based on the meals.
And the AI just builds it, handles the layout,
the buttons, the logic. In minutes, yeah. You
get a working visual tool that solves a real
problem, no coding required. Pretty amazing.
Wow. Okay, next up. Tip six, using branches.
What problem does this solve? This tackles that
really common issue where you're exploring one
idea in a chat, but then you think of an alternative
you want to explore, but you don't want to derail
the main conversation or lose the context. Yeah,
conversation drift. Exactly. Branches let you
create separate parallel conversations that all
stem from the same starting point, the same original
query. So, the trip planning example. You start
by asking about two places, say, Daulat and Fuquak.
Mm -hmm. Then you can create a branch for Daulat
to plan the budget and activities just for there,
and another branch for Fuquak to do the same.
But both branches remember the original context,
like the total trip length or overall budget.
Precisely. The core info remains, but the detailed
exploration happens in separate threads without
confusing each other. Great for comparing options
or exploring what -ifs. Very useful. And that
brings us to tip 7 for this segment. Use projects.
What are these? Think of these as more than just
folders. They're sophisticated, continuous memory
workspaces. OK, so you'd set up a project, maybe
call it Q4 marketing project. Yep. And then you
upload relevant files directly into that project.
Brand style guides, past campaign reports, competitor
analysis. And custom instructions, too. Like,
always write in our friendly but professional
brand voice. Exactly. And here's the key value.
Continuous memory. Anything you put in that project.
Project files, instructions, even past conversations
within it. chat GPT remembers it for every new
chat you start inside that same project. So it's
always building on previous contexts within that
specific project. No more re -explaining the
brand voice every single time. That's the power.
Super efficient. Though, note... there might
be limits on file uploads, depending on your
plan, like maybe five files for free users. Still
very useful. OK, lots of tools there. So for
someone just starting to explore these more advanced
features, which one of these files, canvas, branches,
projects, offers the quickest, most immediate
payoff, like fastest return on investment? Good
question. I'd probably argue using files, especially
that CV critique example. Uploading a document
you already have and getting instant expert level
feedback on its structure and design. That's
immediate value you previously had to pay quite
a bit for. Yeah, that makes sense. Instant expert
feedback is hard to beat. All right, let's move
into segment three. Expert mode. This is about
really leveraging the system for learning, honing
voice, and getting ultimate control. Tip 8 introduces
study mode. Yeah, this isn't just about getting
information from the AI. It's about learning
through a conversation that actively challenges
your thinking. How does that work in practice?
The example prompt is great. Create an AI board
of experts to help evaluate my business idea,
and then you describe your idea. So the example
was healthy meal kits for busy professionals
in Ho Chi Minh City HCMC. Right. And the AI doesn't
just give you facts. It responds by adopting
specific roles. You might say, OK, I'm the operations
expert. Question, how will you ensure delivery
freshness during peak HCMC traffic? Ah, so it's
asking challenging questions from different perspectives.
Exactly. Then maybe it switches. Now I'm the
finance expert. Your proposed price point seems
low. How will you ensure profitability considering
ingredient cost fluctuations? So you're forced
to actively problem solve and defend your idea
against expert level scrutiny rather than just
passively reading info. That's it. It really
pushes your assumptions. Invaluable for stress
testing any idea. OK, next. Combating that robotic
AI tone. Tip nine. Write like a real person.
This is crucial if you want the output to sound
genuinely human. It's a two -step process they
recommend. Step one. Provide two or three samples
of your own writing. Stuff you think sounds like
you. Okay, so it learns your style. And step
two. You give it a framework prompt with some
unbreakable rules. Things like keep most sentences
under 15 words. Use simple vocabulary. Strictly
avoid buzzwords. You know, synergy, optimized
leverage. Oh, yes. Please avoid those. And connect
ideas naturally using simple conjunctions like
and, but, so. So it's a combination. Model your
specific voice, but also enforce general rules
of clear, simple human writing. That's the magic
combo. Makes the output feel much more conversational
and less like, well, like a machine wrote it.
Now, for the really advanced stuff. Tip 10, Agent
Mode. This sounds serious. It is. It requires
a paid plan. But Agent Mode... essentially puts
Chet GPT on autopilot for complex multi -step
tasks. Autopilot? Like what? Think research projects,
browsing the web for information, even designing
visuals. It can string together multiple actions
to achieve a goal you set. Whoa! Okay, hang on.
Imagine scaling that. Having an agent plan an
entire trip, research a whole market report,
and create the presentation slides while you
just sort of watch. That's the potential. It
handles the research, the browsing, the integration
between different tools, like pulling info and
then feeding it to an image generator. Okay,
give me the specific example they used. Planning
a company team building event. The task was...
Plan a two -day event for 30 employees near HCMC.
Budget is 1 .5 million VND per person, about
60 US dollars. That's a complex task. Lots of
variables. And Agent Mode apparently handled
it. It autonomously researched three suitable
resorts nearby, compared them, created a detailed
minute -by -minute schedule for the two -day.
Okay, impressive. And it designed a fun announcement
poster for the event using the built -in image
generator. It delivered the whole package. a
task that would normally take a person, what,
days? Then automatically, that's... Yeah. Yeah,
that's a glimpse of the future right there. It
really is. Now, finishing up with tips 11 and
12, these are quicker hits on maximizing control
and making sure the AI works smoothly for you
over time. Tip 11 covers extra controls. Right.
Little things, but important. Like the edit message
feature lets you tweak your prompt after you
send it without losing the whole conversation
context. Super useful for fixing typos or refining
your ask. Oh, that's handy. No more starting
over for one mistake. And connectors, these are
integrations, usually paid, linking chat GPT
to other tools like your Google Drive or Calendar.
So you could ask it to, say, summarize my unread
emails from the last 24 hours. Integration is
key. What else? Personalities. You can choose
different default interaction styles, like robot
for purely factual, lookner for supportive, cynic
for critical feedback, or nerd for deep dives.
So you can kind of set the default mood for the
conversation. Yeah. And also, the ability to
access older models, like GPT -4, usually tucked
away in the settings if you need them for some
reason. OK. And finally, tip 12. This sounds
important. Manage memory. hugely important, and
probably the most underrated control. ChatGPT
remembers things about you and your preferences
across different chats to personalize responses
that's stored in its memory. Which sounds good,
but... But that memory can get outdated. If you
told it six months ago you were working on Project
X, but now you're on Project Y, it might still
be tailoring answers based on old, irrelevant
context from Project X. Ah, so its memory needs
tidying up. Exactly. You need to regularly go
into settings, personalization and review, edit,
or clear out old memories that are no longer
relevant to your current goals. So keeping the
memory clean ensures the answers stay accurate
and focused on what you need now, not what you
needed last year. Precisely. It prevents context
drift and keeps the AI aligned with your current
reality. It's essential maintenance for long
-term reliable use. OK, we've covered a ton of
features there, from basic mindset shifts to
complex automation and control. Based on everything
we've synthesized, what's the single most underrated
control people skip that really costs them accuracy
over time? I think it has to be that memory management.
It's not flashy, but forgetting to prune that
old context means the AI is working with bad
data about you. Cleaning the memory ensures consistency
and accuracy. It's the foundation for reliable
long -term results. Yeah, that makes perfect
sense. Ongoing maintenance is key. So, wrapping
this up, the big idea, the core takeaway from
this deep dive, really seems to be that the difference
between getting amazing AI results and just OK
results. It isn't really about the AI's raw capability
anymore. No, it's about us. It's about how well
we communicate with it, how clearly we give instructions,
how we provide context, how we guide its thinking.
These 12 techniques we covered aren't just clever
tricks. They feel more like fundamental shifts
in how we need to interact with these powerful
systems. Better communication leads to better
outcomes. Absolutely. So, if you're listening
and want to try just a few things this week,
what would you prioritize? Based on our discussion,
I'd say. One, try using those magic phrases like,
think step -by -step on your next complex prompt.
See if you notice a difference. Good one. Two.
Two, implement that self -critique method. Create
a quality checklist for an important email or
document before you ask the AI to write it. Define
success upfront. And three. Set up your first
project. Upload a key document, like a style
guide or project debrief, and experience that
continuous memory benefit. Solid starting points.
Now, for a final thought to lead people with.
Considering that agent mode's power, we talked
about autonomously researching, planning, designing
complex things. Yeah. And the source material
even hinted at people using these techniques
to build multiple businesses rapidly. Yeah. It
makes you wonder, doesn't it? If you truly master
this kind of AI communication, what parts of
your own work, your own projects, maybe even
parts of running a small business, could you
realistically automate this year? That's a provocative
question. Moving beyond just assistance to actual
autonomous execution of complex tasks, what becomes
possible then? Something to think about. We really
hope this deep dive into the source material
saved you some serious learning time and gave
you practical tools. If you found these insights
useful, maybe share this with a friend or colleague
who's also trying to level up their AI game.
Thanks for joining us for this deep dive.