OK, let's talk about that frustration. When you've
spent maybe 40 minutes, could be an hour, just
trying to get the right words in a prompt, you're
tweaking, you're fixing, and the output, it's
still not quite there. And you just get that
sinking feeling, right? That maybe it would just
been faster to write the darn thing yourself.
Oh, absolutely. That feeling is so common. And
it really comes from treating these AIs like
some kind of magic black box. We're just guessing,
throwing prompts at the wall. Yeah, trial and
error. Exactly and we're wasting so much potential
doing that. So today we're gonna dive into four
Really practical reliable ways to work with AI
techniques that kind of turn that guesswork into
Well predictable results. Yeah getting smarter
about how we talk to the AI. Right. So the mission
today is really clear, then. We want to help
you move past that frustrating loop of back and
forth, getting almost what you want. Yeah, we're
aiming to give you a toolkit, a set of structured
methods. OK. And these steps should help optimize
time, but also, crucially, improve the quality
of what you're getting back from the AI. Precisely.
So the roadmap for this deep dive is super practical.
First up, how to capture that perfect prompt.
the one you need every single time. Second, we'll
look at how you can take one really good piece
of content and multiply it, turn it into maybe
10 different things without losing that core
quality. That sounds incredibly useful. Then
third. Third, a neat trick for finding the weaknesses,
the holes in your ideas before someone else does,
like your boss or a client. Preemptive critique.
I like that. Exactly. Finally, number four. How
to structure those big complex projects so you
don't just get back a giant messy wall of text.
Which happens all the time. Okay, fantastic.
Let's unpack this toolkit then. Where do we start?
We kick things off with what we're calling the
work backwards trick. This one is really the
antidote to all that prompt guesswork. Okay,
work backwards. I like the analogy you used earlier,
finding a light switch in a dark room. Instead
of just flailing around hoping to hit it, you
somehow turn the light on first and then you
ask the AI, hey, draw me a map straight to that
switch for next time. Precisely. You nailed it.
You don't stress about crafting the perfect prompt
right at the beginning. You actually generate
it at the end based on the perfect output. Ah,
okay. So walk us through that. You mentioned
five steps using maybe an email example. Yeah,
let's use a common task. Writing a team email
about a meeting. Step one. Start super simple,
just the basics. Write an email to my team about
the Monday 9 a .m. meeting on the Blue Sky Project.
Okay, and you get back that first draft. Probably
pretty robotic, right? Usually, yeah. So step
two. you immediately give feedback. You look
at it and say, okay, this is way too formal.
Rewrite it, make the tone more friendly, maybe
a bit excited. Oh, and add a quick joke about
Monday mornings. All right, so you're iterating,
then step three is more refining. Exactly. Keep
tweaking. Maybe you say, actually make it much
shorter. It needs to be readable in like under
30 seconds. And you know what? Ditch the joke.
It didn't land. But do add a clear call action,
ask them to prep one idea, and boom, now the
email is perfect. Just how you want it. OK, so
we have the perfect email after a few rounds
of feedback. What's next? This is where the magic
happens. This is the magic step. Step four, instead
of just copying that final perfect email and
being done, you immediately give the AI a new
instruction. You say, OK, now look back at our
entire conversation, analyze everything we just
did, and write me one single prompt. that would
have produced this final email directly right
from the start. Whoa. OK. So you're asking the
AI to reverse engineer its own process to give
you the ideal input. A -sicely. And it does.
It gives you that golden prompt, which leads
to step five. Save that prompt. Test it in a
fresh chat to make sure it works. Then stick
it in your prompt toolbox, Notion, Google Doc,
whatever. Organized. OK. I see the value in having
that golden prompt saved. But playing devil's
advocate here, Doesn't this whole process mean
you're kind of writing the email twice? Isn't
that more work up front? It feels like it, but
it's really investment. Think of a time saved
on all future similar emails. But more importantly,
it genuinely teaches you how to prompt better.
How so? Because you see exactly which words made
the difference. You see that asking for a friendly
and excited tone worked better than just friendly.
Or specifying under 30 seconds read time was
key. You learn the AI's nuances. Ah, okay. So
that upfront effort isn't just about this one
email. It's about learning the specific descriptive
language that gets results across the board.
Exactly. Seeing those patterns highlights the
words that truly move the needle. You become
a better prompter. Got it. That makes sense.
So once we've nailed getting that perfect prompt,
what's the next challenge? Well, that knowledge
is crucial because the next big hurdle is something
called prompt drift, especially when you want
to repurpose content. Ah, yes, prompt drift,
where the AI kind of loses the plot over a long
conversation, right? The context fades and the
outputs get worse. Precisely. It loses focus.
And I'll admit, I still wrestle with prompt drift
myself sometimes, especially if I rush things.
It happens. It does. And that leads us needily
into our second trick, the content multiplier.
This one tackles that common issue, where you
have this amazing piece of content, say, big
report or a great blog post, and it just sits
there. Yeah, collecting digital dust. Right.
So we use the AI like a super fast assistant
editor. We take that one high quality piece and
multiply it out into loads of different formats
suitable for different platforms. OK, I love
this idea. Taking a... Maybe 1500 word article
and spinning it into what LinkedIn posts tweets
and email summary exactly for LinkedIn posts
three tweets and Internal memo you name it, but
the execution needs structure to avoid that drift.
Okay. How do we do it reliably three steps first?
You paste the entire source material into the
chat, the whole blog post, the whole report.
All of it. Second, and this is key, you set the
context. You give the AI a specific command.
Reply only with the single word read once you
have fully processed all of this text. Don't
ask for anything else yet. Ah, so you're making
sure it's actually ingested everything before
you give it the real task, like a confirmation
step. Exactly. It guarantees the model has the
full context loaded. Only after it replies read,
Do you move to step three? Which is asking for
the different formats. Right. Now you say, OK,
based on the test you just read, write me three
tweets. Focus on different tips. Use a casual
but expert tone. Or draft a short email newsletter
summary. Needs a catchy intro and a strong call
to action based on the main findings. Makes sense.
But I guess the quality of the output still depends
heavily on the input, right? The little garbage
in, garbage out rule. Absolutely critical. This
trick only works if you're multiplying your best
stuff. High -quality source material is non -negotiable.
You can't magically make a weak idea strong just
by multiplying it. Good reminder. And you mentioned
a pro tip about the audience. Yes. Always. Always
tell the AI who the new format is for. This fights
prompt drift and improves relevance dramatically.
Just asking for a summary is bad. Too vague.
Way too vague. Asking, write a three bullet point
summary for my busy boss who only cares about
the bottom line business impact. It's good prompting.
Specific audience, specific needs. OK, so that
context setting with the read confirmation, does
that really make a measurable difference? Or
is it just good practice? It makes a huge difference.
It ensures the AI has fully processed your specific
source material before it starts generating.
So the outputs are grounded in your facts, not
just its general knowledge base. It prevents
hallucination and keeps things relevant. Gotcha.
So it guarantees the AI is working off the right
playbook. Okay, that brings us to trick number
three. The other side trick. Now this one, this
might be the most valuable, I think, for high
stakes communication. Things like proposals,
asking for a raise, pitching investors. Okay,
the other side. Sounds intriguing, like seeing
things from another perspective. Exactly. It's
like having a really smart, maybe slightly cynical
friend. play devil's advocate for you. You create
your piece of content, your pitch, whatever it
is. And then you immediately flip the script.
You make the AI act as your toughest critic.
It stress tests your argument before you face
the real deal. OK, let's use that raise request
example. So I draft my email, I outline my achievements,
why I deserve more money, all the good stuff.
Perfect. You've got your draft. Then prompt number
two, right away. What does that look like? That's
what the magic is. You say something like, OK,
AI. Now, you are Jane, my manager. She's super
busy. She has a really tight budget this quarter,
and she's generally risk -averse. Read my email.
What's your immediate gut reaction? Which specific
sentence annoys you the most? And give me the
top three reasons you'd probably say no right
now. Wow. Okay. That's direct. But I can see
the power. It forces you to see your own blind
spots, doesn't it? Totally. When we write something
persuasive, we're naturally defending our position.
We think it's great. Of course. But the audience,
the boss, the client, the investor, they're often
actively looking for reasons to say no or poke
holes in it. This trick helps you find and fix
those weaknesses first. You preempt the objections.
That's incredibly useful. Are there any specific
tips for making this critique really effective?
Yes. Three key things. First, as we just touched
on, be extremely specific about the persona you
want the AI to adopt. Don't just say, be a critic.
Right. Be Jane, the busy, budget -conscious manager.
Or, you're a 50 -year -old chief financial officer.
You hate unnecessary risk. Your main concern
is Q4 cost savings. The more detailed the persona,
the better the critique. OK, specificity is number
one. What's two? Ask for a ranking. Don't just
ask for weaknesses, ask for the top weaknesses.
List the top three weakest points in my argument
in order from most serious down to least serious.
Ah, that helps prioritize revisions. Smart. It
focuses your energy where it matters most. And
the third pro tip, always close the loop. Close
the loop? How? Don't just stop at identifying
the flaws. Immediately follow up and ask the
AI for help fixing them. OK, based on the top
three weaknesses you just pointed out, help me
rewrite the specific sentences that are most
problematic. Turn that critique directly into
actionable improvement. That makes perfect sense.
Identify rank, then fix with the AI's help. But
quick question. If we make that persona too harsh,
like that super skeptical CFO, is there a risk
we over -correct? Make the pitch too defensive
or negative. That's a fair point. You don't want
to water down your core message completely. The
goal is balanced preparation. By focusing on
the ranked weaknesses, you address the most critical
issues identified by that tough persona without
necessarily overhauling everything. It's about
strengthening, not undermining. Right. balanced
prep, focusing on the biggest ranked weaknesses.
Got it. OK, that leads us to the final trick
in the toolkit. Indeed, the blueprint trick.
This one directly tackles that frustrating problem
we mentioned earlier, asking for something complex
and just getting back this huge, undifferentiated,
often generic wall of text. The wall of text,
yes. How does the blueprint help? It forces the
AI to think about structure first before generating
the content. It's exactly like a finalizing the
detailed architectural blueprint for a house
before you pour a single drop of concrete. Okay,
I see. If you spot a flaw in the blueprint, you
fix it instantly, drag a wall, change a room
size. It's easy on paper or on the screen. But
if you find a major structural flaw after the
AI has already generated 10 pages of detailed
interconnected text based on a bad structure...
You have to tear it all down. Hours wasted. Exactly.
Huge waste of time and effort. So the trick is
to insert a specific blueprint request into your
initial prompt. How would that look? Say, for
that travel blog content plan example. Instead
of just saying, write me a content plan for my
travel blog, you'd say something like, I need
a detailed content plan for my travel blog. But
first, outline the standard sections typically
found in a professional content plan. For each
section, just give me a one sentence description.
Do not write the full content plan yet. Uh, so
you're explicitly telling it not to write the
content, just the structure first. Precisely.
That's step one. Step two. You review that structural
outline it provides, it might list things like
audience analysis, content pillars, keyword strategy,
editorial calendar, promotion plan. Right, the
standard components. Then, step three. you correct
the course before the heavy lifting starts. You
look at the blueprint and say, okay, wait, I
already know my audience really well, so remove
audience analysis, and I handle promotion separately,
so remove promotion plan two. I only want you
to focus on content pillars, keyword strategy,
and the editorial calendar. So you approve the
specific sections you want included. You approve
the final blueprint, and then step four, you
tell the AI, okay, now generate the full content
plan, but only based on these specific sections
we just agreed on. The result is targeted, structured,
and exactly what you need. No waste. Wow. Okay,
that's incredibly efficient. You can just imagine
scaling that kind of structured planning across,
I don't know, dozens of big complex business
reports or technical documents. That feels like
real leverage. It really is. True leverage. So,
just to crystallize it. What's the main danger,
the biggest risk, if you skip doing this blueprint
step when you're tackling a really big multi
-part project with an AI? The biggest danger
is wasted time and effort on a massive scale.
You risk the AI generating pages and pages, maybe
10, 20 pages of detailed content based on a flawed
or irrelevant structure. And then you're forced
to scrap most of it and start over rebuilding
from the ground up. Right, you waste hours generating
irrelevant stuff that has to be torn down. Okay,
blueprint first. Got it. Okay, so let's recap
the toolkit we have assembled here. It's pretty
powerful when you put it all together. Yeah,
definitely. We've got the work backwards trick
that's for capturing those perfect prompts and
learning the AIs language. Then the content multiplier
for taking your best stuff and scaling it efficiently
across different formats. Then the other side
trick for stress testing your crucial ideas and
finding weaknesses before anyone else does. Then
finally the blueprint trick, ensuring structured
logical output for complex projects, avoiding
that dreaded wall of text. It really feels like
these build on each other. They absolutely do.
They stack together like Lego blocks of data.
almost. Think about creating a full social media
campaign plan, for instance. You could use all
four. OK, how would that work? Walk me through
it. All right. First, you'd use the blueprint
trick. You'd force the AI to outline the essential
sections of a good campaign plan goals, target
audience, key messages, content assets, schedule,
metrics, before it writes a single word of the
actual plan. OK, structure first. Makes sense.
Then, once you have a draft plan based on that
approved blueprint, you immediately use the other
side trick. You tell the AI, OK, now be a cynical
follower of our brand. You hate feeling sold
to. Read this campaign plan. What feels off?
What sounds too salesy? What part of the schedule
makes no sense? Ah, getting that critical audience
perspective early. Nice. Exactly. Once you've
refined the plan based on that critique, then
you bring in the content multiplier. You take
the main campaign announcement post, maybe, and
tell the AI, OK, turn this into five short Instagram
story scripts, a longer email for our subscriber
list, and bullet points for a short explainer
video. Multiplying the core message efficiently.
Got it. And the last piece. Finally, because
that whole process, blueprint, critique, multiply,
worked so well and gave you great results, you
use the work backwards trick. You ask the AI.
Analyze this whole workflow. Give me the optimized
set of props that achieve this entire campaign
plan and asset generation. And you save that
sequence for the next campaign launch. Wow. OK.
Seeing it stacked like that really shows the
power. Blueprint, then other side, then multiplier,
then work backwards to save the whole workflow.
That's a system. It really is a system. And as
you start using these, just remember the two
big pitfalls we talked about. Yeah. Critical
mistakes to avoid. Right. Number one was being
too vague when you use the other side critique,
wasn't it? Exactly. Don't just say be a critic.
Be super specific about the persona, the role,
the concerns, the biases you want the AI to embody.
Specificity and critique. And number two. Forgetting
to close the loop. Don't just find the weaknesses
with the other side trick. Always take that next
step. Ask the AI to actively help you rewrite
and fix the specific sentences or sections it
identified as flawed. Turn insight into action.
Identify, then immediately fix. OK, so for listeners
wanting to try this out, what's the best way
to start? It feels like a lot to implement all
at once. Yeah, definitely don't try to master
all four overnight. That's overwhelming. The
best approach is to pick the one trick that addresses
your biggest pain point right now. OK, tackle
the biggest frustration first. Exactly. If you
find yourself constantly wasting time tweaking
initial prompts, start with work backwards. Spend
an hour really nailing that process for one type
of task you do often. Makes sense. Or, if you
have a really important presentation or proposal
coming up, try the other side trick today. Stress
test your arguments before you finalize them.
Just pick one method, apply it to a real task
this week, and just observe the difference it
makes. That focused approach feels much more
manageable. Start with one, feel the benefit,
then maybe add another later. Precisely. And...
You know, ultimately, the real deep value here
isn't just about getting slightly better output
from the AI, although that's great. What's the
deeper value then? By consciously using these
methods, forcing the AI to reflect on its process,
to critique from another perspective, to outline
before creating this game, you're actually training
yourself. You're building your own critical thinking
muscles, your own strategic discipline. Demanding
structure from the AI forces you to become a
more structured thinker. That's a really interesting
final thought. We become better thinkers by demanding
better thinking processes from the tools we use.
I like that. It's a powerful feedback loop.