What if you could ask an AI just a simple question,
like one sentence, and still get consistently
better results than someone else writing this
huge, complicated essay prompt? Yeah, it sounds
kind of wild, but it's actually where things
are heading. The secret isn't just the instruction
anymore. It's not just what you type. It's really
about what the AI already knows about you, about
your business. And that's really the core of
this deep dive, isn't it? We're seeing this shift.
away from, let's call it traditional prompt engineering.
The old way, yeah. Towards something new, context
engineering. Exactly. And the mission here for
you listening is how to take that AI that, you
know, kind of generic forgetful assistant and
really transform it, make it into this personalized,
always on team member. Turning a general tool
into a specialist colleague. I like that. So
we've broken it down into five levels, a framework.
Okay. And we're going to walk you through them.
Starting from the AIs. like basic knowledge,
all the way up to giving it this permanent business
memory. But maybe we should start with why this
matters so much. Because it's not just about
getting slightly better answers, is it? There's
some pretty serious research coming out. Yeah,
absolutely. Places like MIT, they're finding
some disturbing things about passive AI use.
I saw that. It's actually, well, shocking. Using
AI like a crutch, the studies show it can make
people measurably... Measurably dumber. It's
a real effect. They call it cognitive outsourcing.
Basically, if you let the machine do all the
heavy lifting, all the synthesis, the analysis,
your own brains, you know, mental muscles, those
neural pathways, they start to weaken. Atrophy,
like a muscle you don't use. Precisely. You lose
that habit of deep thinking, of connecting the
dots yourself. So if I'm just asking the AI for
strategic summaries all the time, and I'm not
really wrestling with the information myself.
challenging it, I'm actually hurting my own abilities.
That's the risk. It degrades the very skills
you need for like critical thinking and making
smart decisions. But the solution isn't to just
stop using AI. Not at all. It's about changing
how you use it, making it an active partner,
something that sharpens you, doesn't replace
you. Okay. That really sets the stage. Active
engagement through context. So let's dive into
level one, the foundation, the AI's basic knowledge.
Right. Level one, the training data. This is
that massive ocean of information the AI learned
from. Trillions of documents, websites, books,
everything. So it knows a lot of general stuff.
A ton. But critically, zero personal context.
It doesn't know you, your company, your goals.
It's like that brilliant stranger at a party
idea. Yeah. You know? Super smart, can talk about
anything. Yeah. But their advice, it's often
totally off because they don't know your specific
situation, your budget, your team, your history.
Exactly. We see this all the time with like a
generic marketing plan prompt. You ask for one.
And you get something that looks impressive.
Lots of pages, bullet points. Looks comprehensive.
Yeah. But dig in. It's often flawed. The audience
might be wrong. The tactics totally irrelevant
for you. Maybe it even makes up details about
your product. And this kind of leads into that
whole model size myth, right? People get so obsessed.
My AI has trillions of parameters. No, mine has
more trillions. It's mostly noise at this point.
Honestly, a distraction. Context beats complexity
every single time. What matters isn't the raw
size or who made the model. Hold on. Are you
actually saying. That maybe a smaller model,
maybe even one that's a couple of years old.
Yeah. If I feed it all my specific customer data,
my internal documents, that could be better than
the absolute latest, biggest flagship model out
there right now. Yes. That's exactly what I'm
saying. It sounds counterintuitive, I know, because
that's not what the vendors are shouting about.
Right. But it's the reality we see in the results.
A smaller model, if it's super rich with your
specific context, will consistently give you
more relevant, more useful output than a giant
model that's just guessing about who you are.
It's about relevance, not raw host power. Okay.
Okay. That's a big shift in thinking. So if model
size isn't the be all and end all, and we haven't
added our own data yet, what's the very first
thing we can do? Like right now? To get better
results without adding anything personal. We
need to get smarter about what's already there.
Move past the generic training data and start
exploiting the AI's own internal rulebook. Ah,
okay. That takes us to level two. The hidden
system prompts. You're saying every AI kind of
has this secret instruction manual. Pretty much.
Think of it like a hidden document, maybe 100,
120 pages long, built by the developers. It defines
its personality, its safety rules, how it should
behave by default. And we can't just go in and
edit that. No, no. That's locked down for safety,
security reasons. But you can definitely exploit
it by learning its triggers. Specific words or
phrases that kind of poke the AI and make it
access deeper. more analytical parts of its programming.
Okay, interesting. So for something like Claude,
you've mentioned there are specific keywords.
Yeah, there are several. You can kind of group
them. Some are about depth, words like in -depth
or comprehensive. Makes sense. Others are more
about actions, like analyze, evaluate, assess.
These tell it not just to spit information back,
but to actually think about it. structurally.
And then there are triggers for the output itself.
Right. Like asking it to research something or
specifically make a report, just using one or
two of these. It can seriously boost the quality,
a huge jump for minimal effort. So back to that
generic marketing plan example. Yeah. Instead
of just asking for a plan, you say, give me an
in -depth, comprehensive marketing strategy.
And the difference is what? It's night and day.
You go from maybe a simple list of ideas to a
much more thoughtful analysis. It might start
talking about market size, competitors, real
business factors, actual strategy. Wow. Okay.
Tiny change, big impact. So that's level two,
exploiting the hidden rules. How do we then take
like permanent control, shape how it talks to
us all the time? That brings us to level three,
user preferences. This is your control panel,
your set it and forget it layer. Like custom
instructions in ChatGPT or personal preferences
in Cloud. Exactly those. You define how you want
it to communicate once and it sticks. It applies
globally to all your future chats. And you focus
on three main changes here that are transformative.
Yeah. Three core things. First, format preference.
Be demanding. Tell it you always want answers
in, say, bullet points with nested sub bullets.
For clarity. Okay. Consistent structure. Yeah.
Second, enforce conservative analysis. You literally
instruct it. Give me realistic, grounded advice.
No pie in the sky stuff. It keeps the output
anchored in reality. I like that. Practical advice.
And third, this one's critical. Certainty levels.
Certainty levels. Yeah. Tell the AI that for
every recommendation it makes, it must state
its confidence level. Like, I'm 70 % certain
this approach will work based on the data. So
if it says it's only like... 65 % sure about
a pricing strategy. Exactly. That's your cue.
It forces you, the user, to dig deeper, to ask
clarifying questions right where the AI is weakest
or the data is maybe less clear. It encourages
that active engagement we talked about. Okay,
so level three locks in the communication style,
the format, the tone, globally. Since this changes
everything permanently, how does setting that
foundation get the AI ready for the really specific
stuff like our own business intelligence? Well,
by getting level three right, you guarantee a
certain baseline quality and consistency. The
output is reliable. It's structured. It's clear.
That makes the AI much better prepared to ingest
and then intelligently use large amounts of complex,
specialized, high stakes information without,
you know, getting confused or giving you messy
results. Midroll sponsor. read all right we're
back and we've reached level four project knowledge
you said this is maybe the most transformative
level absolutely this is where we really give
the ai that memory upgrade a working reliable
memory so it sucks being just a general tool
right and it starts acting like a like a fully
onboarded team member someone who's been with
you a while knows the ropes knows our products
our pricing key metrics who our competitors are
That level of detail. Exactly. It assimilates
your specific business context for a particular
area of work. And how do we actually do that?
How do we implement it? It usually involves creating
project specific workspaces, you know, like those
separate chat sidebars you see in Claude or dedicated
project folders and other tools. And then into
that specific space, you upload your documents,
your proprietary stuff. Like what kind of documents?
Could be anything relevant. Past client proposals,
market research reports, detailed lists. of customer
pain points, testimonials, your strategic planning
docs, competitor analysis files, Google Docs,
PDFs, whatever holds that key project knowledge.
You know, I have to admit, I still wrestle with
prompt drift myself sometimes. You get deep into
a conversation and you realize the AI has kind
of forgotten what you were talking about 10 turns
ago. Oh, totally. It happens to everyone. But
level four, building these dedicated project
spaces, it forces an organizational structure.
It gives the AI and you an anchor, making the
whole process way more reliable. It combats that
drift. So how does it work technically? Does
it just read the documents once? It's often using
something called Arrage. That's retrieval augmented
generation. Basically, when you upload documents
to a specific project, the AI builds a mini searchable
knowledge base just for that project. And then
for every single question or task you give it
within that project space, it automatically searches
and pulls relevant information from those documents
you uploaded. It's like an always -on dedicated
memory for that specific task or client. Whoa.
Okay, hang on. Imagine you ask it for a quarterly
strategy review. And it automatically pulls in
the latest competitive analysis you uploaded
last week and references specific customer quotes
from testimonial docs without you having to copy
paste or remind it about any of that background.
That's it. Exactly. That's genuine scaling. It's
not just an assistant. That's like a personalized
copilot. Precisely. Upload once, leverage it
forever within that context. It becomes deeply
specialized. Okay. Mind kind of blown there.
So what's left? What's level five? Level five
is the peak. The apex, really. AI -generated
prompts, or you could call it meta -prompting.
Meta -prompting. Yeah. Now we're actually using
the AI itself to help us engineer the perfect
prompt for a really complex task, using the machine
to build the instructions for the machine. Okay,
walk me through that. What does that initial
request look like? It sounds... Complicated.
It is a bit. The initial request is high level.
You might ask the AI, OK, generate a new comprehensive
prompt template for creating a marketing strategy
for this specific new customer segment. And you'd
add constraints. Oh, yeah. Yeah. You tell it.
This prompt needs to ensure the final strategy
includes specific team OKR's objectives and key
results. It must analyze insights from our past
customer complaints database and factor in recent
competitor wins. You get the idea. You're defining
the structure of the ideal prompt. Got it. So
the AI takes that complex request and it uses
everything it knows now. The base training data,
the system triggers, my preferences, and all
the specific project documents. All four layers,
exactly. And it uses them to generate this incredibly
detailed, tailored prompt template specifically
designed for that task. And then you take that
AI generated template. You feed it back into
the AI in a new session, maybe tweak it slightly.
And boom, the output isn't just a strategy doc.
It might be a whole package. Sales enablement
guides, scripts for handling objections, even
frameworks for calculating ROI, all hyper -specific
to that context. Feels like a massive leap in
efficiency. It is. Think about it. That level
of thoughtfulness, of cross -referencing documents,
aligning with goals, that used to take a team
maybe two, three weeks. Easily. Lots of meetings
back and forth. Now you can get that quality
of output, that integrated thinking in minutes,
that time saving. That's where the real competitive
edge comes from. So just to be clear, this isn't
about context replacing prompt engineering. No,
not at all. They're partners. Think of it like
this. Context, levels one to four, is the perfectly
tuned, fully loaded race car. It's got the engine,
the fuel, the specs. Prompting, level five in
your ongoing instructions, is the skilled driver
who knows the track and how to get the absolute
best performance out of that car. You need both.
That's a great analogy. Okay, so we have this
powerful framework, levels one through five,
potentially saving weeks of work. Given all that
power... What's the single biggest mistake people
make when they start trying to implement this?
What did we absolutely avoid? Stop trying to
write war and peace in your prompts. Seriously.
Once you've put in the work to establish the
context, especially levels three and four, your
instructions should actually get simpler, shorter,
more direct. If the AI already knows your entire
business strategy level four, you don't need
to re -explain it every time. Just give it the
core task. Let the context do the heavy lifting.
Simplify the instructions because the context
is doing the work. Got it. Okay, let's recap
the big picture here. The fundamental shift.
The secret sauce. It's setting up all this context
before you even ask the main question. That's
the core idea. We went through the five levels,
starting with the AI's generic knowledge, L1,
exploiting its hidden rules, L2, setting our
preferences, L3, giving it project -specific
memory, L4, and finally, getting the AI to help
write its own instructions, L5. And the good
news is you listening can start doing this right
away. We've got a simple three -step roadmap
for you, your context mastery roadmap. Okay,
step one, what's the immediate action? Today,
take maybe 10, 15 minutes. Go online and research
the system prompt for the AI tool you use most
often. Just search your AI tool name plus system
prompt. Find those trigger words we talked about.
Like in -depth, analyze, evaluate. Exactly. And
just finding them isn't enough. The key is to
start using them. Try adding just one or two
to prompts you already use. See the difference
yourself. It's usually immediate. Okay, quick
win, step two. Tomorrow morning, go into your
AI settings, find those user preferences or custom
instructions, and lock in those three core settings
we discussed. Demand bullet points or your preferred
format, insist on conservative, grounded advice,
and require it to state its certainty level for
recommendations. Set it and forget it for consistent
quality. And step three. By the end of this week.
Pick one project, one area of your work. Create
a dedicated workspace for it in your AI tool.
And just upload three to five relevant documents.
Just start small. Start small. Give it something
specific. Maybe your company's mission statement,
a key product description, a list of customer
FAQs. Something rich so it stops giving you generic
fluff and starts understanding your world. Right.
Build that initial memory. So research triggers
today, set preferences tomorrow, upload a few
files by week's end. That's the start. It feels
like organizations that really lean into this,
that master this context engineering, they're
going to gain a pretty significant edge, won't
they? In just speed of decision making, strategy.
Huge advantage. Almost unfair, like you said.
When your AI understands your business deeply,
the speed and quality of strategic work just
accelerates dramatically. So the final thought
here is... Do this work. Put these context layers
in place thoughtfully, and your AI will finally,
truly start working with you as a partner, not
just churning out generic text for you. So maybe
think about it. What's the first area of your
work you're going to transform by giving your
AI a real memory and context?