Have you noticed lately how much AI generated
content can feel? Well, a bit generic. Oh, definitely.
It's everywhere. Social media fees, blogs. It's
often flooded with this kind of digital sameness.
It all just sounds so alike. Yeah. Kind of like
AI music. Exactly. Yeah. But what if there was,
you know, a strategic advantage, a way to actually
break free from that mediocrity and make AI outputs
not just interesting, but like. actually profitable
and scalable for your business. Right. Moving
beyond just playing around with it. Welcome to
the Deep Dive. Today, we're going to unpack that
powerful technique, JSON prompting. Ah, yes.
The good stuff. And this isn't just some clever
trick, right? It feels like a fundamental shift
in how we talk to these intelligent systems.
for real business impact. That's exactly it.
We'll dive into why AI results can be so inconsistent
first off, then explain what JSON actually is
and why it's so different from those traditional
prompts most people are using. The conversational
ones. Yeah. Then we'll get into some real -world
business models that are actually powered by
this structured approach. Oh, cool. We'll even
walk you through the, let's say, the logic of
creating your first JSON prompt. It's not as
scary as it looks. Good to know. And then, crucially,
how to test these things rigorously, how to improve
them, and maybe the most important part, the
pitfalls to avoid, the things that'll trip you
up. Okay. So our mission today is really to shortcut
your understanding of this method because it
feels like a genuine game changer for building
profitable, defensible businesses with AI. It
really can be. OK, let's unpack this. So starting
with this sea of sameness in AI content, it's
a real challenge, isn't it? It really is. The
Internet is just, well, brimming with low quality
templated stuff, articles, videos, images. And
so much of it looks and sounds the same. And
you're saying it's. because millions are just
using really basic prompting. Pretty much. They're
using simple techniques. Our source material
actually poses this fascinating question. When
you prompt an AI, are you a poet or an engineer?
Poet or engineer? So, okay, what's the difference?
Well, the poet uses creative conversational language,
you know, descriptive. Like talking to a person.
Exactly. But the engineer, they're all about
highly structured data -like formats. Very precise.
Okay. And the source also mentioned a debater
and a gambler. Yeah. The debater is that endless
back and forth trying to negotiate a better result.
I think we've all been there. Oh, yeah. And the
gambler just types something in and, well, hopes
for the best, crosses their fingers. Right. So
how does this connect to the results? Well, the
harsh reality is if you're still using a simple
conversational prompt like, write an article
about plants. Mm -hmm. You're competing with
millions getting those same generic results.
Your content just gets lost in the noise. It
doesn't stand out. Because the instructions are
too vague. Exactly. A prompt is just the instructions
you give to an AI, right? If your instructions
are vague, your output's going to be vague. Garbage
in, well, maybe not garbage out, but generic
out. So if these basic prompts lead to this undifferentiated
kind of junk content, what's the fundamental
shift JSON brings? JSON prompts provide a detailed
blueprint for consistent, high -quality AI output.
A blueprint. I like that. Okay, so what is this
powerful format? Tell us about JSON. Okay, so
JSON stands for JavaScript Object Notation. Fancy
name, but it's basically just a standardized
way to organize data. Really clean, really structured.
So instead of talking to it conversationally.
Right. Instead of those vague prompts, you give
the AI this detailed blueprint. It tells the
AI exactly what to do, how to structure the output
and what rules it needs to follow. OK, got it.
Think of it like this. The chef's analogy is
great here. A regular prompt. That's like telling
a chef, hey, cook me dinner. Right. Could be
anything. Could be amazing. Could be. Well, not
so amazing. It's a gamble, right? You gave no
real instructions. But a JSON prompt? That's
like giving the chef a super detailed recipe.
Exact ingredients, precise measurements, step
-by -step instructions. Okay, so the result isn't
a guess. Not at all. It's predictable. It's high
quality. Every single time. Or at least much
closer to every time. And that's the core benefit
then. consistency and reliability exactly because
that predictability is what lets you take some
fun little ai tool and turn it into an actual
profitable business that can scale up precisely
that's the leap so when you compare them side
by side regular versus json prompting the differences
sound pretty stark they really are regular prompts
often generic inconsistent output json It's structured.
It's predictable. And scalability. Almost impossible
to replicate consistently with regular prompts.
But JSON, it's designed for reuse. You just tweak
the variables. Yeah, and for business applications.
Regular prompts are kind of limited, maybe for
one -off brainstorming. JSON, though, that's
the engine for tools, for APIs, for automated
workflows. It's built for business process. APIs.
Just quickly, remind us what that is. Oh, right.
An API application programming interface is just
a way for different computer programs to talk
to each other. It sends data back and forth in
a structured way. Gotcha. And cost efficiency.
You mentioned JSON can actually be more efficient.
That seems counterintuitive. Yeah, it sounds
like more work up front. But because the instructions
are so compact and structured, you often use
fewer resources, fewer tokens in AI terms over
time. So those API calls can actually cost less,
especially at scale. Interesting. So yeah, it
all boils down to control. You stop being just
a hopeful requester. Crossing your finger, yeah.
And you become more like a system architect.
You're designing the output. OK, so why is this
structured approach, this control, specifically
valuable in modern business like right now? Because
modern business runs on structured data. JSON
lets AI speak that language. That's a huge point.
Everything is data. It really is. It's like the
universal language for computers now. Everything
we interact with, words, images, customer behavior,
even DNA, it can all be represented as data.
And to truly work effectively with AI, you have
to learn to speak this language. It's not optional
anymore, not if you want serious results. So
the big tech companies get this already. Oh,
absolutely. Look at Google, Microsoft, Netflix,
Amazon. They're betting multi -billions on structured
data. They know it's the most valuable asset
on the planet, arguably. How are they using it?
Well, Netflix uses your viewing data, structured
data, to decide which shows to make. Amazon uses
purchasing data, structured data for recommendations.
They've built entire empires on refining and
acting on structured data. So data is the new
oil, but structured data is the refined fuel.
Is that a fair way to put it? That's a great
analogy. Yeah. And it suggests that maybe most
businesses are still kind of stuck dealing with
the crude oil stage when it comes to AI. So where's
the biggest bottleneck then in that refinement
process, getting from crude idea to refined AI
fuel? Honestly? Often it's the human element.
It's taking those messy, unstructured human ideas
and translating them into clean, calculable data
that an AI can actually work with effectively.
It's a translation task. It really is. And it
becomes like a professional reflex. One expert
we looked at said, if I ever used AI to get a
really good result, I would take that result
and turn it into a JSON prompt so I can get that
again. Ah, so capture the success, systematize
it. Exactly. Don't just get lucky once. Engineer
it so you can repeat it. Okay, this makes sense
conceptually. But beyond the theory, what specific
business models are actually using this JSON
approach right now? Businesses are using JSON
for automated content. webinars, custom tools,
and boosting efficiency. Lots of areas. Right.
So this isn't just theoretical pie in the sky
stuff. It's happening now. Absolutely. It's very
real. Think about, say, content as a service.
Okay. Businesses are desperate for high quality,
consistent content. They know they need it, but
often they don't know how to prompt effectively
to get it. So there's an opportunity there. Huge
opportunity. You can step in as their outsourced
AI content strategist. You could sell libraries
of pre -built, battle -tested JSON prompts for
things like blog articles, social media posts,
email sequences. Or you could offer done -for
-you services, where you use your internal JSON
systems to generate the content for them, or
even license your systems out to agencies. So
you're selling the structured process, not just
the end result. Exactly. And it goes beyond just
blog posts. We saw an example of an entrepreneur.
who had this webinar script, generated $14 ,000
in revenue in just a few hours. pretty good well
that so what they did was deconstruct its winning
formula the hooks the structure the calls to
action and turned it into a detailed json prompt
template wow now they can generate new high converting
webinar scripts basically on demand just change
the variables for the product the audience boom
new script that's that replication advantage
you mentioned yeah extract the success make it
repeatable that's it scale the process with perfect
consistency what else another huge area is building
and selling a API tools, sometimes called Microsoft
little software as a service tools. There's massive
search volume online for simple AI tools. Think
AI text generator, AI logo generator, AI summary
tool. People will happily pay a small fee for
a tool that solves one specific problem really
well. And you could build these with JSON prompts.
Yeah, you can build them yourself, often using
no -code or low -code platforms like Bubble or
SoftR. You don't necessarily need to be a hardcore
coder. How did that work? Well, first, you kind
of deconstruct success. Analyze the best existing
tools for a specific task. What makes them...
work okay reverse engineer right then you engineer
a master json prompt that replicates those results
maybe adds your own improvements this prompt
is the brain of your tool got it finally you
build a simple interface a web page maybe this
interface takes the user's input like their company
name or the text they want summarized it feeds
that input into your master prompt via an api
call to an ai like chat gpt and the ai sends
back the structured result exactly yeah User
gets their logo idea, their summary, whatever
it is. Simple problem solved. That's clever.
And then there's business automation. Yeah, high
-value business automation. Think about small
and medium businesses. Many are just drowning
in repetitive manual tasks. Sorting customer
emails, summarizing long reports, drafting standard
documents. Things that take up hours but don't
necessarily require deep strategic thinking.
They're actively looking for ways to automate
this stuff. So you can step in there too. Definitely.
You can become a business process automation
consultant, a BPA consultant. Your core offering
is building custom JSON prompt systems tailored
specifically to their workflows. Can you give
an example? Sure. Imagine a law firm. They get
hundreds of emails a day. Someone has to read
each one, figure out who it's from, what it's
about, which case it relates to, how urgent it
is. It takes hours. Yeah, I can see that. You
could build a system with a JSON prompt. It analyzes
each incoming email. extracts the key info client
name case type urgency level maybe even summarizes
the main point and categorizes it automatically
that would save a ton of time dozens of hours
per week easily maybe more so you're not just
selling you know content generation you're selling
raw efficiency and that's incredibly valuable
to businesses whoa Imagine scaling that law firm
example, like automating thousands of hours across
an entire industry. The sheer volume of manual
work out there that could potentially be transformed.
Yeah. It's kind of immense when you think about
it. It really is. The potential is huge. So beyond
just getting better output, what do you think
is the deeper cognitive shift here? What really
changes for someone who... embraces this. Got
to be about thinking differently, right? Thinking
in terms of data and systems, almost like the
AI itself does. Exactly. That's the core of it.
You've really hit on the most important transformation
there. It's mental. Mastering JSON, it forces
you to stop thinking in these vague sentences
and start thinking the way an AI actually thinks,
or at least processes information in terms of
data rules and systems. And this systematic approach,
it gives your business or your projects like.
Five superpowers. Ooh, superpowers. Okay, what
are they? All right, first, consistency. Your
results become predictable, reliable, no more
guesswork. Second, scalability. Your systems
can work at pretty much any volume without a
drop in quality. One request or a million, the
process holds. Crucial for growth. Third, efficiency.
You generally get faster results and, like we
said, potentially lower API costs over the long
run. Good. Fourth, professionalism. Your output
can be designed to automatically match industry
best practices or specific formatting requirements.
Okay, and fifth? A competitive moat. Your unique,
systematic approach, the specific way you've
structured your JSON prompts, that's incredibly
difficult for competitors to just copy. It becomes
a real asset. A defensible advantage. Precisely.
An expert in the field really underscored this,
saying, in the age of AI, you've got to understand
this because that's going to give you the leg
up. It's about differentiation. So the real upgrade
isn't just the prompt. It's thinking like a computer.
It upgrades your brain's operating system, maybe.
I like that. Yeah, it moves you from traditional
kind of vague thinking to structured systems
thinking. So traditional thinking is like, write
me something good about elephants. Okay, very
poet -like. Right. But systems thinking is more
like, generate content about elephants that follows
this proven engagement pattern, includes these
specific structural elements like a hook and
key facts, uses this tone, and is designed to
achieve this measurable outcome, like click -through
rate. Much more engineer -like. Much more specific.
Exactly. So this isn't just about writing better
prompts for ChatGPT. It's about learning how
to communicate effectively with any complex automated
system. It's a fundamental skill for the future.
This all sounds incredibly powerful. But for
someone new to this, what's the most common mental
block or maybe the biggest hurdle they'll face
when trying to actually adopt this new way of
thinking? I think many people get stuck seeing
JSON and thinking it looks like code, like programming,
and that can be intimidating. The real challenge
is shifting your view, seeing it not as code,
but just as highly organized instructions, like
a very, very detailed recipe. Okay, just structured
instructions. I know JSON can look a bit intimidating
at first glance, you know, with all those curly
braces and commas and quotes. Yeah, the syntax
can seem weird. But you're right. If you think
of it as just organizing your instructions clearly,
maybe it's less daunting. Exactly. Let's maybe
walk through the logic of creating one, say for
writing a high converting product description.
How would you approach that systematically? Okay.
Good example. All right. Step one, define your
goal clearly and be specific. Don't just say,
write a description. Right. Too vague. A much
better goal is something like. Generate a persuasive
product description that must include a catchy
title, a list of 3 -5 key features, a list of
2 -3 emotional benefits, and a strong call to
action. See the difference. Yeah, much clearer.
It has requirements. Exactly. Step 2. Break your
goal into logical sections. Think like that computer
again. What are the core data fields you need
the AI to understand or fill in? Okay, so for
the product description. Maybe the product name
itself. Yep. Product name. What else? Who it's
for. The target audience. Good one. Target audience.
We mentioned key features. Maybe specify that
as a list. Right. So key features as a list.
And emotional benefits also as a list. Perfect.
Maybe tone of voice. Like playful or professional.
And definitely the culture action. Okay. So these
become like categories for our instruction. Exactly
right. Then step three. You actually write your
JSON structure using these categories. You format
them using keys. That's the label, like product
name in quotes and values, which are your specific
instructions or the variables the user will input.
So it looks kind of like a structured form. That's
a great way to think about it. You're telling
the AI, here's a field for the product name.
Fill this in. Here's a list for features. Populate
this. Here's another list for benefits. Use this
specific tone. You're building a very precise
questionnaire for the AI. Okay, that makes sense.
What's next? Step four, you have to instruct
the AI to use your structure. You've got to be
expert. explicit about it. You can't just assume
it knows. Nope. You literally tell the AI something
like, based on the following JSON object containing
product details and instructions, generate the
product description. Your response must also
be in a valid JSON format using a single key
called product description, which contains the
full text. Ah, so you tell it how to respond
too. Why is that important? It ensures the AI
uses your input correctly and gives you clean,
predictable output that another system like your
website can easily understand and use. No weird
formatting issues. Got it. Structured input,
structured output. Bingo. And finally, step five.
Reuse, repurpose, and build your library. Once
you have a JSON prompt that works well... Don't
just use it once. Exactly. It's a reusable asset.
Swap out the variable's product name, features
for different products, tweak the tone of voice
for different marketing campaigns or niches.
So you capture the successful structure. Capture
it in JSON and reuse it. Build up that library
of proven prompts. You know, I have to admit,
I still wrestle with prompt drift myself sometimes,
even using JSON. Oh, yeah. What's prompt drift
again? That's when, you know, an AI's output
kind of... subtly shifts or deviates over time,
even if you're using the exact same prompt you
used last week. The model updates, things change.
Right. It's not always perfectly static. Yeah.
So keeping it consistent, even with JSON, it's
definitely a continuous process of monitoring
and tweaking. It's not always set and forget.
That's a really good point. Maintenance is key.
Can you give us maybe a full concrete example
of a more complex system built this way, something
beyond just a product description? Absolutely.
Let's think about that AI logo generator system
we mentioned earlier, how you could reverse engineer
design principles to build it. Okay, the logo
generator. Let's dive into that. Okay, so the
core idea behind an AI logo generator system
built with JSON is kind of like reverse engineering
a masterpiece painting. How so? Well, you first
study the techniques of the great masters, in
this case, great logo designers. You figure out
why their work is effective. Then you create
detailed instructions based on those techniques.
And finally, you build a robot painter, your
AI system, to execute those instructions. Okay,
so step one is studying the masters. Yeah. Research.
Exactly. So step one is the research phase. Think
of it as your art history phase for logos. You
start with a specific research prompt. A prompt
just for research. Yeah. You'd ask the AI to
analyze, say, 100 successful logos across different
industries, look for common patterns and color
schemes, font styles, shape simplicity, how well
they fit the industry, memorability. And you'd
ask it to output those key patterns in a structured
way, maybe even JSON. So you're using AI to figure
out what makes a great logo in general. Precisely.
You're extracting the design principles. Okay,
then step two must be building the actual generator
prompt. Yep. Step two, the system creation. Writing
the master prompt, you take all those insights
from your research phase, those principles, and
you use them to build a really sophisticated
logo generator JSON prompt. This prompt would
take simple variables from the user, like their
company name and industry, but it would apply
fixed rules based on your research. Things like
appropriate color psychology for that industry,
guidelines on font pairing, principles of shape
and simplicity. So you'd find keys like task,
company name, industry, maybe preferred style.
And then crucially, you'd include sections like
color psychology rules and design principles
that contain the distilled wisdom from your research
phase. The AI must follow these rules. So the
user gives simple input, but the prompt applies
complex expert rules. That's the magic. And then
step three is the implementation. building the
actual robot painter. The tool itself. Right.
You create a simple web tool, maybe using bubble
or software like we mentioned. Just a couple
of input fields. Company name, industry. Okay.
When the user hits generate. The tool sends your
master JSON prompt filled with their input and
your embedded rules to an AI API like ChatGPT.
And the API sends back? It sends back maybe several
professional looking logo concepts, potentially
a design rationale explaining why certain choices
were made based on the rules, and maybe even
usage guidelines for the new brand. All structured,
ready to display. Wow, that's a pretty sophisticated
system built on that structured prompting idea.
But how do you make sure these powerful systems
actually work reliably? You know, that they don't
just break or give weird results sometimes. The
million dollar question. You have to battle test
them rigorously, consistently. And then you keep
refining them. It never really stops. Battle
test. I like that. So it's not enough just to
write the prompt. Absolutely not. A prompt that
hasn't been rigorously tested. It's just a guess.
It's a hypothesis. To build a reliable AI system,
you must battle test your JSON prompts. Find
their breaking points before your users do. Okay.
How do you do that? Is there a process? Yeah,
think of it as a continuous two -stage process.
There's the initial stress testing and then the
ongoing improvement. Okay, let's start with the
initial stress test. Right. Our source suggests
a neat three -part framework for that. First
is the consistency test. Makes sense. Run the
exact same prompt, same variables, at least 10
times. Measure the variation in the output. Are
the results wildly different each time? If so,
your prompt isn't constrained enough, you need
to refine it, add more rules or structure, until
the output is reliably consistent. Okay, test
for consistency. What's second? Second. The performance
test. This is where you A -B test. Pit your shiny
new JSON prompt against a traditional conversational
one for the same task. Ah, a direct comparison.
Yeah. And measure the real -world difference.
Does the content generated by your JSON prompt
get higher engagement, better click -through
rates, higher conversion rates, whatever your
metric is. Document those improvement percentages.
Prove its value. Okay, performance metrics. And
third. Third is the chaos monkey test. Fun name,
right? Yeah. What's that? This is edge case testing.
You intentionally try to break your prompt, feed
it weird input, nonsensical variables, unexpected
data types, things users might accidentally do.
Why? To make sure it handles errors gracefully.
Does it give a helpful error message? Does it
have a sensible fallback response? Or does it
just crash or worse, give you completely bizarre,
unusable output? You need to know how it fails.
Okay. Consistency, performance, chaos monkey.
That's the initial testing. What about the long
game? Right. The long game is continuous improvement.
Your AI system is never truly finished because
the AI models themselves are always evolving.
So like monthly reviews? Yeah, something like
that. Monthly reviews. Look at your analytics.
Which prompts are performing best? Which ones
are generating the most value? Find the patterns
in your winners, the structural elements, the
phrasing, and use those insights to update your
entire prompt library. Level everything up. In
the longer term. Quarterly optimization. The
AI landscape changes fast. New models come out.
New features get added. So every quarter, you
should probably test the latest AI models and
capabilities. That state -of -the -art prompt
you wrote three months ago, it might already
be obsolete or inefficient compared to what's
possible now. Wow. Okay, so you need to archive
underperforming prompts. Yep. Archive the old
ones. Rebuild prompts to take advantage of new
AI features. Keep it current. It sounds like
a lot of work, but necessary if you want a robust
system. Now, you mentioned pitfalls earlier.
Things that can kill your system. Ah, yes. The
rookie mistakes. Think of them like things a
beginner chef might mess up. Okay, what's pitfall
number one? Pitfall one. The overly complicated
recipe. This is trying to build incredibly elaborate,
deeply nested JSON structures right from the
start. You think more detail is always better,
but... It can confuse the AI. Exactly. Sometimes
simpler is better. The fix. Start simple. Get
the basics working reliably. Then add complexity
gradually, only when it's truly necessary to
get the control you need. Okay, start simple.
What's next? Pitfall 2. The hard -coded menu.
This is where you hard -code specific details
like a particular product name or a date directly
into the JSON prompt template itself. Why is
that bad? Because it turns what should be a scalable
reusable template into a rigid one -off command.
You can't easily adapt it. The fix. Always ask
yourself which parts of this need to be changeable
variables and which parts are fixed rules. Clearly
separate the flexible inputs from the fixed structure.
Makes sense. Keep templates flexible. Pitfall
3. Pitfall 3. Serving an untasted dish. This
is the big one, assuming your prompt works perfectly
without actually testing it rigorously. Just
writing it and deploying it. Yeah. Dangerous.
The fix is obvious. That'll test every single
prompt before you rely on it in a real system
or for a client. Use that three -part framework.
Consistency, performance, and the chaos monkey
test. No excuses. Test everything. Got it. And
the last one. Pitfall four. Using last year's
recipe book. This is related to the continuous
improvement point. It's using the same prompts
indefinitely while the underlying AI models are
evolving at a breathtaking pace. Sticking with
what worked six months ago. Right. That prompt
you perfected for GPT -4 might be inefficient
or suboptimal with GPT -5 or whatever comes next.
The fix. Regularly review and update your prompt
library. Treat it like a living document, not
a stone tablet carved once and forgotten. OK,
those are four really practical pitfalls to watch
out for. So wrapping this up, what's the ultimate
takeaway here for listeners really looking to
thrive in this whole AI era? I think it's this.
The future belongs to the system builders, those
who can translate ideas, needs and goals into
structured data that AI can act upon reliably.
System builders, not just prompt pinkers. That's
a really powerful summary. It feels like the
age of just casually typing random prompts, you
know, hoping for a good result. Yeah. That's
shifting. Maybe it's already over for serious
applications. I think it is. Yeah. And JSON prompting
isn't just some, I don't know, hack. It feels
more fundamental. Like the bridge between those
messy one -off outputs we all get sometimes and
the structured repeatable systems that actually
form the backbone of real defensible businesses
using AI. It truly is that bridge. When you learn
to think like a computer or at least communicate
with it systematically using JSON, you stop wasting
time and money on inconsistent results. Right.
You start creating actual predictable value through
repeatable workflows, reliable content pipelines,
and those monetizable tools and services we talked
about. And the best part maybe is that a powerful
JSON template becomes this asset, right? It works
for you 24 -7, churning out. Consistent quality.
Exactly. It's your strategic advantage. It's
your shortcut to quality and maybe your way out
of that junk content land. So where is this heading?
What's next for JSON prompting? Well, looking
ahead, I think we'll see JSON prompts starting
to coordinate multimodal stuff, text, image,
video, audio, all potentially within a single
structured command. Imagine asking for a blog
post and the accompanying social media images
in one go. Wow. OK. We'll probably also see more
automated optimization. Right. Maybe AI systems
that can fine tune their own JSON prompts based
on real time performance data. Whoa. AI optimizing
AI prompts. Yeah. And eventually. Probably more
industry standardization around these structured
formats, making it easier for different AI systems
and tools to talk to each other seamlessly. Okay,
so for listeners who want to get started, what's
the essential toolkit? What do they need? Well,
obviously the AI platforms themselves. ChatGPT
is great for text. Claude is strong on complex
reasoning. Gemini has powerful multimodal capabilities.
Pick the right tool for the job. Right. And then
some basic development tools can be really helpful.
Things like online JSON validators, just to quickly
check if your syntax, your brackets and commas
are all correct. Saves a lot of headaches. Good
tip. And maybe a simple code editor, even something
basic like VS Code or Notepad++, just to create,
organize, and manage your library of JSON prompts
more easily than using a plain text file. Okay.
And we should mention detailed examples of those
JSON structures we discussed, like for the product
description and logo generator, will be available
in our show notes for people to look at. Definitely
worth checking out. So maybe a final thought
for our listeners. If you're serious about leveraging
AI for profit or even just for reliable results
in your work, it really feels like it's time
to stop just playing with prompts and start engineering
systems. Well said. Because that's where the
future is being built, isn't it? So the question
for you is, what system could you start designing
today? Good question to ponder. Thanks for joining
us for this deep dive.