You ever ask your super smart AI for something
amazing and what you get back sounds like, I
don't know, a screeching cat? Uh -huh. It feels
like someone handed you this incredible instrument,
a Stradivarius, but you only know how to play,
like, beginner scale. Yeah. All that power, but
the results. Kind of disappointing. Yeah, and
that AI sameness you see everywhere online, where
everything just sounds flat. Exactly. We're going
to dive into why that happens and, more importantly,
how we can fix it. Welcome back to the Deep Dive.
Today, we are unpacking a framework that could
really change how you talk to AI. It's called
Race Eco. Think of it like the sheet music for
your AI. It helps turn that generic noise into
something, well, symphonic. No more bland cookie
cutter outputs. We'll go through each part. Role,
instruction, context, examples, constraints,
and output format. Six key things. And then we'll
touch on a simpler version for everyday stuff
and a way to really refine your prompts over
time. Yeah, the goal here is to help you shift
from just, you know, throwing prompts out there
to actually being an architect of the AI's response.
Crafting stuff that's genuinely exceptional,
tailored every time. Let's make some music. So
a lot of us, myself included sometimes, we just
talk to AI like we're chatting, right? Expecting
magic. But that often just leads to that, that
sameness, that flood of content that sounds like
a robot wrote it. Right. If your prompt is just
write an article about plants, well... You're
basically just adding to the noise floor online.
Yeah. The AI is powerful, yeah, but it's definitely
not reading your mind. And that's where this
RACIQ framework comes in. It's a more structured
way, a systematic set of instructions. Like that
sheet music idea. It guides the AI past the generic
stuff. Helps it produce something, you know,
actually beautiful, exceptional. It's a six -step
formula. Kind of like a chef getting everything
prepped, the mise en place before cooking. For
perfect AI output. You might not need all six
steps for every single prompt. That's true. But
knowing them, that's the key to really leveling
up what you create. Okay. So let's start at the
beginning. What's that very first ingredient
if you want a unique perspective from the AI?
It really comes down to giving the AI a specific
persona, a character or point of view to adopt.
Right. R for role. So assigning a role. It's
like casting a really good method actor in a
film. Exactly. instantly changes the whole vibe.
The tone, the words it chooses, its perspective,
everything shifts. Imagine telling your AI, okay,
now you're a board -certified sleep doctor. Right.
And then for the next prompt, telling it, okay,
switch gears, now you're an absolutely exhausted
parent trying to give advice to other parents.
The difference is going to be massive. So let's
take that sleep advice example. If you just ask,
give me advice on how to get better sleep, what
do you get? Probably something pretty generic,
like a basic Google search result. Technically
okay, but totally uninspired. But if you add
the role, you are a board -certified sleep doctor
with 20 years of clinical experience. Give me
advice. Then, suddenly, the output changes. It
gets concise. It's evidence -based. It probably
uses more clinical terms. Feels like a professional
consult. Or the other way. You are a sleep -deprived
parent writing a blog post for other exhausted
parents. And boom. It's conversational, it's
empathetic, maybe even a little funny, full of
practical tips that actually resonate with parents
who are in the trenches. Wow. So the basic task,
give sleep advice, didn't change at all. Nope.
But the role fundamentally shifted how the AI
approached it, how it thought, how it spoke,
not just what it said. That's pretty powerful.
Just defining who the AI should be changes the
nuance that much. And here's a pro tip. Try the
expert panel prompt. You actually ask the AI
to embody. Multiple roles at the same time. Maybe
conflicting roles. Like a skeptical professor,
a super optimistic VC, and maybe a pragmatic
engineer all arguing about AI consciousness.
Exactly. It forces the AI to explore different
angles, generate a more nuanced discussion, moves
beyond just one simple answer. Great for brainstorming
complex topics. Okay, so we've cast our actor
with the role. Now, how do we make sure they
know exactly what scene they're in, what to do?
That comes down to giving it really clear, precise,
unambiguous commands. Got it. I is for instruction.
The core action you want the AI to take. And
vagueness here is your enemy. Vague instructions.
You get vague, generic results every time. You
got to think like a, well, like a good commander.
You said, go take that hill isn't enough. Right.
It needs to be. Take that hill. Flank from the
east. Use available cover. Establish defense
by sundown. See the difference. Precision. This
is where the specificity principle comes in.
You need to ditch those fuzzy words like engaging
or cool or professional. Yeah, those are like
empty calories for an AI. It doesn't know what
you really mean by engaging. It's just guessing.
Let's say you want a YouTube short script. A
weak instruction is, write me an engaging YouTube
short about haunting tips. The AI just throws
darts in the dark hoping to hit engaging. But
a strong instruction, maybe something like...
Write a 60 -second YouTube short script on prompting
tips. Start with a strong curiosity gap hook.
Include a scroll -stopping visual anchor in the
first three seconds. Now you've given it concrete,
objective things to aim for. You translated your
subjective desire engaging into specific parameters.
So instead of just asking for creative, you might
say, write in the style of a 1940s hard -boiled
detective novel. Exactly. Precision is totally
power when you're talking to AI. And there's
this technique, chain of thought prompting. Ah,
yeah. That's a really useful one. You basically
tell the AI, hey, before you give me the answer,
show me your work. Think step by step. Okay.
So for anything involving complex logic or math
problems or reasoning, telling it to outline
your entire chain of thought step by step makes
a huge difference in accuracy. And you can see
how it got the answer. More transparent. Precisely.
All right. So the AI knows who to be, role, and
what to do. Instruction. What else does it need
to really understand the situation, the world
around the task? It needs the backstory. The
context. You need to give it the details about
the audience, the purpose, the background, the
tone, its whole worldview for this task. See,
for context, this is the whole setting the story
so far. You said this is the step people skip
most often. Yeah, I think so. And it's like asking
an actor to perform without telling them anything
about the play or their character's history.
The result is going to be flat, give it motivation,
the full scene. So you mentioned four pillars
for great context. First. Audience. Who's this
for? Beginners. Experts. Loyal customers. Second,
background. Where does this live? Is it for a
business blog? Social media? A specific scenario.
Okay. Audience. Background. Third, purpose. Why
are we even doing this? To teach something. To
persuade. To sell. Just to entertain. Yeah. And
fourth, tone. How should it sound? Witty. Super
formal. Empathetic. Casual. Let's take an example.
Write a 500 -word blog post about AI video tools
without context. You get a generic overview.
Probably not very useful. Yeah. Just a list of
tools, maybe. But if we add context, audience,
non -tech, small business owners, background.
It's for a marketing agency's blog. Purpose dot.
Get them excited about turning podcasts into
YouTube shorts easily. Tone. Simple, practical,
inspiring. See, now it's not just a generic list.
It becomes a highly targeted, persuasive piece
that actually speaks to those business owners
about their needs. Totally different outcome.
And you shouldn't be afraid to give it a lot
of context for complex tasks, like a brain dump.
Absolutely not. AI can process pages of text
almost instantly. Better to give it too much
background than too little. Over -deliver on
the context. What about for ongoing projects?
Like if you're always writing for the same brand
voice? That's where a persona document comes
in super handy. It's like a master file. With
all your standard context, your audience profile,
your brand voice guidelines, your company mission,
stuff like that. So you create this document
once. And then for future prompts, you just tell
the AI, hey, first, read and fully internalize
this persona document. Everything you generate
needs to align with it. It's a huge time saver.
It keeps everything consistent. Okay. Roll, instruction,
context. We're building a really detailed prompt.
Now, how do we make sure the quality or the style
of the output matches what we want? Something
really specific. Ah, that's where examples come
in. You show, don't just tell. Provide concrete
examples of excellent output. Mid -roll sponsor
read insert point. Okay, we're back. We were
just talking about ensuring the AI output matches
a specific quality or style. You said E for examples
is key. Yeah, absolutely. If you need a very
specific structure or a particular tone or a
certain format, showing the AI really high quality
examples, that often has the biggest, most immediate
impact on the output. It's like training an apprentice,
right? You don't just describe the perfect report,
you show them one. Exactly. It's way faster.
This technique, by the way, is often called few
shot prompting. You're giving the AI a few shots,
a few examples to learn from directly. Let's
think about a newsletter. If you just say, write
a newsletter about the top three AI news stories
this week. You'll likely get a blob of text,
probably unstructured, maybe kind of dry, generic.
But if you provide an example from a previous
week, you say, act as my newsletter co -writer.
You need to follow the exact same tone, style,
and format as this example below. And then you
paste in your best one. Now the AI gets it. It
sees your custom sections, your specific way
of phrasing things, your intro style, your outro.
It learns not just what topics to cover, but
how you actually write the newsletter. The quality
jump must be significant. Oh, it's huge. And
this isn't just for writing. Remember, you can
use examples for anything structured. Getting
code output in the right JSON format. Complex
logic patterns. Show it an example. That makes
sense. And you had a pro -level tip for sales
and marketing here. Yeah, this one's clever.
Try giving the AI an anonymized email or inquiry
you receive from your absolute ideal customer,
the perfect lead. Okay. The AI analyzes their
language, the specific pain points they mentioned,
their professional tone, everything. Then you
ask it to craft new outreach messages or marketing
copy based on that analysis. So it learns how
your best customers actually think and talk.
Precisely. It helps you create messages that
really resonate deeply with that specific customer
profile. Super powerful for targeting. We're
building quite a prompt here with RACE, role,
instruction, context, examples, what's needed
to keep the AI on track. Stop it from just drifting
off into generic AI speak. That's where constraints
come in. You need to set clear rules, boundaries,
the rules of the game for the output. The second
C, constraints. Okay, so. If context is the worldview,
constraints are the hard and fast rules. Exactly.
Think about any game. It needs rules to be fun,
challenging, interesting. Without rules, it's
just chaos. Your AI needs those boundaries, too,
to force it to produce focused, high -quality,
professional output. Because by default, these
models can be a bit verbose, a bit hedging. Yeah.
They often default to being wordy, playing it
safe. Constraints are how you tame that. You
force it to be concise, maybe more opinionated,
stay aligned with exactly what you need. So what
are some examples of effective constraints? Well,
they can be structural. Your response must be
under 100 words or include exactly one direct
quote and one statistic. They can be stylistic.
Do not use corporate buzzwords like synergy or
leverage or the tone must be warm and conversational,
not formal. Or even persona -based, like the
output needs to sound like it came from a competent
founder, not an academic. Precisely. Or even
do not use any hashtags. They are lame. You're
giving it direct commands to shape the final
output. And for managing these over time, especially
on big projects. You can add your core constraints
right into the AI's custom instructions or settings
if the platform supports it. It's like establishing
the official rulebook for that AI on that project
ensures consistency. Now, you mentioned a negative
constraint technique. What's that? This is really
useful when you notice the AI keeps making the
same mistakes or falling into bad habits. You
explicitly tell it what not to do. Oh, okay.
So you'd literally say something like, do not
use exclamation points. Do not use marketing
buzzwords. Do not make the response longer than
150 words. Exactly. It's very direct. It helps
quickly steer the AI away from those default,
often annoying AI sounding patterns and towards
something more unique. I have to admit, I still
wrestle with prompt drift myself sometimes. The
AI just seems to forget the earlier instructions
or constraints over a longer conversation. Oh,
totally. It happens. Negative constraints are
a great tool to help tighten that up when you
see it starting to drift. So we've guided the
AI to create some amazing content using RICEC,
role, instruction, context, examples, constraints.
How do we make sure the final presentation is
useful, immediately usable? Right. That final
step is crucial. You need to specify the exact
structure, the presentation, how you want the
information packaged. Oh, for output format.
This is about how the AI structures the response.
Yeah. Think of it like having a really fine wine.
Just getting the wine, the content isn't enough.
A messy wall of text. That's like dumping the
wine straight onto the table. Oh, okay. The output
comment is the crystal glass. The perfect vessel.
It presents the content cleanly, makes it organized,
easy to understand, and importantly, ready to
use. And it's not just about making it look nice,
is it? Not at all. It's about making it actionable,
sometimes even machine readable. A well -structured
output like a neat table or a JSON object can
often be plugged directly into other software,
other automations. Saves a ton of manual reformatting.
What are some common formats we can ask for?
Oh, lots. Simple tables are great for comparisons.
JSON is fantastic if you need structured data
for apps or APIs. It's kind of the universal
language for that. Markdown is good for creating
web -ready text or documents with basic formatting
like headings and lists. You can even ask for
a Twitter thread structure. Absolutely. Format
this as a five -tweet thread. It'll break it
down accordingly. Let's take that software comparison
idea again without specifying format. You get
dense paragraphs. Hard to compare tool A versus
tool B versus tool C. But if you add the constraint,
present your answer as a three -column markdown
table. Columns, tool name, key features, ideal
use case. Instantly usable. Perfectly formatted.
Easy to scan. You get the information you need
at a glance. That single line about format makes
all the difference. Okay, here's a thought. Could
you ask for the same information in multiple
formats in one go, like a chain of formats? Whoa.
Yes, you absolutely can. That's a really efficient
technique. So I could ask for, say, a summary
of an article and request it as a LinkedIn post
and as a five point bullet list for Twitter and
maybe that three column comparison table. all
from the same core content in one single response
you got it that is a passive productivity hack
especially for content repurposing generate a
whole week's worth of social media posts formatted
for different platforms with just one smart prompt
that's seriously impressive okay so after going
through all six rice eco steps role instruction
context examples constraints output format What
does a really top -tier prompt actually look
like when it all comes together? It looks like
a precise, highly structured blueprint, something
that consistently yields customized, actionable,
and genuinely valuable results, not just generic
text. Let's try that real estate example. The
bad prompt was just, how can I use AI and automation
in my real estate business? Yeah, super vague.
You'll get a useless, generic answer that doesn't
help your specific business at all. But a Rice
Eco -enhanced prompt would be much more detailed.
Right. You define the role. You are a business
growth strategist specializing in real estate
tech. The instruction. Identify the top three
AI opportunities for my specific business. Create
a prioritized action plan focusing on time savings
and revenue generation. Okay, then add context,
details about the agency size, location, current
challenges, specific goals, maybe the tech staff.
Then maybe examples. Here's an example of a lead
qualification automation that worked well for
a similar agency. Add constraints. The solutions
must be implementable by a non -technical person
within a $500 monthly budget and require less
than 10 hours setup time. And finally, the output
format. Present this as an AI and automation
playbook with clear sections for opportunity,
action steps, estimated time saved, and required
budget. You see the difference. The output from
that prompt is going to be completely customized.
It addresses the specific market, the budget,
the time limits, the unique pain points. And
you mentioned it could even project things like
8, 12 hours of weekly time savings. Yeah, because
you gave it the context and constraints needed
to make those kinds of specific relevant calculations.
It's not generic advice anymore. It's a tailored
strategy. Now, that full Ristoko framework seems
incredibly powerful for these big, complex tasks.
But you also mentioned it might be overkill for
simpler everyday prompts. That's right. For probably
80 % of the things you ask an AI to do day to
day, you don't necessarily need all six steps
every single time. You can get fantastic results
focusing on what I call the ICC method. ICC,
instruction, context, and constraints. Exactly.
Think of it as the 80 -20 of prompting. These
three are often the most... crucial ingredients
for getting good results quickly so the must
-haves a clear instruction what exactly should
it do essential context who is this for why does
it matter and clear constraints the key boundaries
or rules nail those three and you'll already
be way ahead of most generic prompts let's try
that twitter example again with just icc the
weak prompt was write a twitter post about chat
gpt tips generic forgettable output guaranteed
but a strong icc prompt might be Instruction.
Write a Twitter thread. Give me five practical
lessons learned from using chat GPT daily. Okay.
Context. My audience is indie creators and solo
entrepreneurs. They want practical AI tips, not
hype. Got it. And constraints. Keep the tone
clear, non -technical, and casually insightful.
No marketing buzzwords or hype. No hashtags,
please. Perfect. That ICC prompt is clean, clear,
and gives the AI enough direction to produce
something much, much better than the vague version.
It hits the core requirements. So Rye Seco for
complex stuff, ICC for the daily grind. Makes
sense. But even with a great prompt, the first
output isn't always perfect, right? You said
the real magic is often in the follow -up. Absolutely.
Crafting the perfect prompt often doesn't happen
on the first try. It's an iterative process.
That's where the EIO process comes in. Evaluate,
iterate. Write and optimize. E -I -O. Okay, first
E is for evaluate. So don't just accept the first
thing the AI spits out. Right. Put on your detective
hat. Really interrogate the results. Don't just
passively accept it. Ask clarifying questions.
Like what? Ask things like, what assumptions
did you make when generating this? Or what context
might be missing here? And my personal favorite
power move. Now critique your own output. Be
brutally honest about its flaws. Whoa! Asking
the AI to critique itself. Yeah, it's surprisingly
effective. It forces the AI to perform self -analysis,
often revealing weaknesses or areas for improvement
without you having to figure it all out yourself.
Okay, so after evaluating, I is for iterate.
This is the back and forth. Exactly. This is
the creative partnership part. You collaborate
with the AI, refine the output. Don't be afraid
to be a demanding director. So you'd say things
like, OK, rewrite this paragraph to be more concise.
Or can you add a bit more humor here? Or give
me three different versions of that opening sentence.
Yep. It's this rapid conversational iteration
that helps you discover new angles, nail the
tone, and really perfect the message. And finally,
O is for optimize. What does that involve? Once
you've iterated and arrived at an output that's
perfect, don't just stop there. optimize the
original prompt itself, incorporate everything
you learned during the evaluation and iteration
phases. Ah, so you're improving the recipe for
next time, making a reusable tool out of that
perfect result. Exactly. You're creating the
reusable mold from your perfect sculpture. And
you can even ask the AI for help with that step
too. For sure. You can literally say, okay, analyze
our entire conversation, especially my... Corrections
and requests. Now rewrite my very first prompt
so that it would produce this final perfect output
directly on the first try. That's brilliant.
Turning a successful one -off interaction into
a reliable, reusable asset. So when we pull all
this together, RISCO, ICC, EIO, what's the really
big takeaway here about how we should be thinking
about AI, our role in it all? I think the biggest
shift is moving away from just being a passive
prompter. Someone just asking basic questions.
And becoming. Becoming a thoughtful architect,
an architect of the AI's interaction, its reasoning,
its output. The Riceco framework, then, it's
not just about technical tricks for writing prompts.
It's more fundamental, a mindset shift. Exactly.
It's about moving from making vague wishes to
providing a clear, detailed blueprint for the
AI to follow. And the benefits are faster workflows,
fewer revisions. and consistently more professional,
more reliable, more useful results. It all boils
down to communicating with the AI using clarity,
structure, and precision. So for everyone listening,
the next time you sit down to work with an AI,
think ICC for those quick daily tasks, instruction
context constraints. Get those right first. And
for the bigger, more complex projects, bring
out the full Rice Eco framework. Role, instruction,
context, examples, constraints, output format.
Master this way of thinking, this structured
approach, and you really will unlock the true
potential of AI. It becomes less of a magic box
and more of an indispensable partner. Because
ultimately, as we've kind of unpacked today,
every truly great AI output. It really does start
with a great prompt, a well -architected prompt.
Couldn't agree more. So maybe something to think
about. What possibilities does this kind of structured
prompting open up for your work, your creativity?
Worth considering as you go out and architect
your AI future. Thanks for diving deep with us
today. Until next time.