Have you ever felt that profound frustration,
trying to coax genuinely brilliant results from
AI only to get back something? Well, kind of
generic. Oh, damn good. I still wrestle with
prompt drift myself, you know? Yeah. That feeling
of sending the same instruction and getting slightly
different, less useful outputs over time. Yeah,
damn right. It turns out there's maybe a deeper
secret to unlocking AI's true power than just
crafting the perfect prompt. Welcome to the Deep
Dive. We unpack complex ideas into clear, digestible
insights, really revealing the crucial knowledge
you need to stay ahead. Glad to be here. Today,
we're diving into a concept that's rapidly changing
how we work with artificial intelligence, context
engineering. Yeah, this is a big one. It's essentially
about teaching the AI about your world, right?
Your specific universe. Not just firing off commands.
Exactly. Not just isolated commands into the
void. We're going to explore what context engineering
is, how it fundamentally differs from traditional
prompt engineering. Yeah, and show how it changes
things. And show you exactly how it transforms
AI outputs from just functional to, well, truly
extraordinary. We'll walk through some compelling
real -world examples. We'll tackle that intriguing
Goldilocks problem. Yes, not too much, not too
little. the challenge of providing just the right
amount of information, and then we'll introduce
a powerful framework to help you get that balance
perfect. By the end of this deep dive, you should
have a clear, actionable path to building AI
workflows that truly understand what you need
and deliver consistent, really impactful results.
So many of us use powerful AI tools like ChatGPT
or Cloud, daily maybe? Yeah, they're everywhere.
But often the responses feel flat. A bit uninspired.
Solace sometimes. Yeah. You might give it a simple
command like, write a follow -up email, and what
you get back is totally templated, generic enough
to apply to anyone. Which means it connects with
no one, really. Exactly. That's a super common
experience, isn't it? The core issue is that
AI, for all its brilliance, often operates in
a vacuum. Kind of like a blank slate. No background
knowledge. Right. It lacks the implicit knowledge,
the tribal wisdom of your specific business,
your unique customers, your goals. So even with
a great prompt, it's basically guessing. Context
engineering is about filling that void, giving
the AI the nuanced understanding it needs to
stop guessing and start truly. contributing.
Like onboarding a new team member? Exactly. It's
like providing a new team member with your entire
company's institutional knowledge on day one.
That's a powerful analogy onboarding an AI. So
how does this environment building, this deep
immersion, actually differ from what most people
think of as basic prompt engineering? Well the
fundamental shift is about connecting the AI
to dynamic continuously updated information and
crucially integrating it into multi -step business
processes. So it's about making AI a truly informed
dynamic partner in the process. Precisely. Moving
from isolated commands to an AI that actually
understands and acts within your workflow. OK.
Let's unpack those two core differences then.
First, you mentioned dynamic context. Traditional
prompt engineering might put static info right
in the prompt. Like write this in a professional
tone. Right. Exactly. And that static approach,
it has its place. Sure. But with context engineering,
you connect the AI to live data sources. Live
data? What do you mean? Imagine an always -updating
Google Sheet. Maybe with daily customer interactions.
Or your CRM system feeding real -time sales data.
Wow, okay. Or even direct. real -time info streaming
from APIs. This continuous data flow keeps the
AI operating with the absolute freshest, most
relevant information. And the second difference
you mentioned, workflow integration. This is
where I think it gets really interesting for
businesses. Oh, this is truly where the magic
happens. Context engineering doesn't just operate
in isolation, you see. It's integrated into larger,
often pretty complex business processes. Like
a sales process. Exactly. Think about a 10 -step
sales process. Maybe three or four of those steps
involve AI agents. Each agent in that sequence
accesses specific, relevant context for its particular
step. And crucially, it builds on the outputs
from the previous AI or even human steps. Ah,
so they pass information along. Precisely. It
creates this seamless, intelligent value chain
that moves towards a defined business outcome.
That idea of AI agents building on previous outputs.
That's fascinating. Can you give us a concrete
example of something that really brings it to
life? Absolutely. Let's look at content marketing.
We can compare the output of just a generic request
versus a blog post generated with real context
engineering. Okay, so dynamic context means fresh
info, and workful integration means connected
actions. You got it. Real -time intelligence
powering interconnected strategic AI tasks. All
right, so imagine you just tell your AI, write
a blog post about the benefits of cloud computing
for small businesses. Standard request. Right.
The result, it'll probably be correct, technically
sound. But boring. But almost certainly incredibly
generic. Yeah. It's the kind of piece you read,
you nod, and you immediately forget. It just
doesn't resonate. That's the typical old way.
a basic standalone request. Now, with context
engineering, you don't just ask for a blog post.
You build an entire workflow around it. A workflow?
How? Well, your AI now has access to real -time
data from your Google Search Console, so it knows
the exact keywords your audience is actually
searching for. Okay, that's smart. It pulls from
your customer persona document. So it understands
their pain points, their aspirations, their language.
It consults your brand voice guide to make sure
every word aligns with your company's identity.
Right. And it can even access your latest product
catalog, complete with features and pricing for
specific offerings. Wow. So your prompt isn't
just a command anymore. It becomes something
far more powerful, almost like a detailed project
brief for a human expert. Exactly. You're asking
it to write a compelling 1 ,200 word blog post,
maybe, but then you layer in all that critical
context, specific GSE keyword data, precise customer
pain points, your brand voice guidelines. And
details on your unique product packages, like
your starter cloud or growth engine. Specifics
matter. The result isn't just an article. It's
a profound, highly targeted piece. It speaks
directly to your ideal customer. in their own
language, addressing their specific challenges.
And promoting your solutions. And subtly promoting
your specific solutions. That's the transformative
power. It moves from just general information
to a real strategic asset. That content marketing
example really clarifies things. It makes me
wonder, how accessible is this really for an
average business? How do we actually... build
one of these, you know, sophisticated contextual
workflows ourselves. It's actually more accessible
than you might think. It basically involves four
key steps. Four steps, okay. Setting up a trigger,
adding an AI step, connecting those dynamic sources,
and then crafting a truly contextual prompt.
So context really takes a simple request and
makes it strategic. Exactly. Turning generic
ideas into profoundly relevant and effective
communication. Okay, this is where the rubber
meets the road then. What's step one for someone
looking to implement this? Step one. Set up your
trigger. This is the signal, right? The thing
that kicks off your workflow. Like what? Could
be a new lead appearing in your CRM, could be
a contact form submission on your website, or
even just a schedule time, like every Monday
morning at 9 a .m. Okay, so the trigger defines
when it starts. Exactly. When the AI springs
into action. Then, step two, add your AI step.
This is where you choose your AI model, maybe
Claude III Sonnet for creative writing. or GPT
-4 for analysis. Depending on the task. Right.
But the magic that elevates this beyond just
basic prompting, that really happens next, doesn't
it? Right. Step three, connect dynamic context
sources. This is where you plug in all that live
relevant information. This can mean data from
previous steps in this workflow. It could mean
pulling in linked info from external tools like
your CRM or customer support databases, or even
dynamic knowledge files like Google Docs or Sheets
that your team keeps updated. So living documents.
Exactly. And you can even use more advanced stuff
like model context protocol connections that
allows for structured programmatic access to
potentially vast internal data sources, so the
AI can query and retrieve really specific information
as needed. And finally, step four, craft your
contextual prompt. OK, so now your prompt isn't
just a request. No, it's much more. It's a meticulously
designed set of instructions that integrates
and leverages all that rich dynamic context you've
provided. It's a blueprint. Yeah, like giving
the AI a blueprint filled with precise, relevant
details. Think about crafting a personalized
follow -up email to a potential client. Okay.
Instead of some generic template, you'd pull
in the customer's exact name, their interaction
history, right from your CRM. Specific pain points
they mentioned, maybe from a knowledge file.
and even the latest product updates from a Google
Doc. The AI isn't guessing anymore. It's synthesizing
current relevant data to create a truly bespoke
message. That makes perfect sense. But I can
imagine a potential pitfall here. Is there a
danger of giving the AI too much information?
Or maybe too little. Absolutely. That's precisely
what we call the Goldilocks problem in context
engineering. The Goldilocks problem. OK. You
need it to be just right. So it's about carefully
curating the right live information for each
specific AI step, avoiding overload, but also
scarcity. Yes, exactly. Making sure the AI has
exactly what it needs when it needs it. Nothing
more, nothing less. The Goldilocks problem. Finding
that sweet spot. What exactly happens if you
go too far? If you give an AI too much context.
Several issues pop up, and they can be quite
costly, actually. How so? First, higher financial
costs. Most AI APIs charge by tokens. Tokens,
right? Like little pieces of text? Exactly. Small
pieces of text, words, or parts of words. Every
bit of context you feed it adds to this token
count. So verbose input directly hits your budget.
OK, makes sense. This also seriously impacts
the AI's context window. Which is like its short
-term memory. Yeah, basically, the finite amount
of text an AI model can hold in its active, immediate
memory at one time overwhelm that window, and
the AI just struggles to filter the noise. It
gets less focused. So it loses track. It can
lose the thread of your main instruction, yeah.
And frustratingly, it can even increase the risk
of hallucinations. Where it makes stuff up. Where
the AI just invents information, exactly. Imagine
scaling that to a billion queries, each one overloaded
with data. The costs alone. That'd be staggering.
Huge. So, giving it your entire 50 -page business
plan just to write a short social media post
is, well, counterproductive. Completely. What
about the other side, then? What happens if you
provide too little context? Well, that leads
directly back to those generic kind of soulless
responses we talked about earlier. Yeah, the
flat stuff. There's a complete lack of personalization.
and a much higher chance of the AI making incorrect
assumptions because it simply doesn't have enough
specifics to work with. Like asking for directions
without saying where you want to go. Pretty much.
It's like asking for a social media post without
mentioning the product, or the audience, or the
call to action. It's essentially useless. The
Goldilocks problem isn't just about technical
limits. It's really a strategic design challenge.
It sounds like precise curation of context is
just as crucial as the prompt itself, like turning
AI from a blunt tool into a surgical instrument.
Well put. So how do we find that sweet spot?
The source mentions something called the real
framework, R -E -A. Ah, yes. The real framework
is your guide, basically, to providing perfect
context. OK. What does it stand for? R is for
relevant. only include information that directly
helps the AI complete its specific task. No fluff.
Got it. Relevance. E is for efficient. Provided
concisely. No redundancy. Think maybe a one -page
summary instead of that 20 -toach doc. Efficient.
Okay. A is for accessible. Your context has to
be readily available to the AI. Ideally, connecting
to live databases or structured knowledge files,
it can't be locked away. Accessible. Right. And
L is for logical. Structured information works
best. using clear formatting, like Markdown,
perhaps, and consistent terminology throughout.
Don't confuse it. Relevant, efficient, accessible,
logical, real. So the real framework helps make
sure our context is perfectly tuned. Exactly.
It's about quality and precision over just sheer
volume, making sure you get maximum impact. This
brings us to another really crucial point, I
think. Most people, when they first start, probably
get this wrong. How should we actually organize
and structure our knowledge files for the AI?
So it's both logical and accessible. Yeah, this
is key. First, avoid what we call the flat file
fallacy. Flat file fallacy. That's just dumping
absolutely everything into one giant sprawling
document. Don't do that. OK, so what instead?
Instead, embrace a modular design. Break your
knowledge into smaller interlinked modules. Think
of it like stacking Lego blocks of data. Lego
blocks. I like that. You might have a company
info .md file, a separate brand voice guide .md,
maybe a solder dedicated to customer personas.
Each one neatly categorized. And tagging documents
with metadata seems smart too, like version 2
.1 or audience. Internal. Absolutely. Metadata
is key for organization and for retrieval later.
Also include examples. Examples. Yeah. If you're
providing a brand voice guide, don't just give
it rules. Show it do and don't. Examples of actual
sentences. Ah, practical examples. Makes sense.
And critically... establish a process to keep
it updated. Information changes, right? Your
knowledge base has to be a living thing. You
can even, in a kind of interesting twist, ask
the AI itself how it would prefer you structure
your knowledge files for it to consume them optimally.
Really? Ask the AI. Yeah, why not? Get its input.
So a well -structured modular knowledge base
is really the foundation for getting intelligent,
reliable AI outputs. Yes, absolutely. It's the
bedrock for truly intelligent, reliable, and
consistent AI performance. Okay, let's talk about
some advanced strategies now. Advanced concept
one, context chaining and refinement. This sounds
like making the context itself smarter as it
moves through a workflow. It is, essentially.
Imagine a complex customer support process. Okay.
Okay. Step one. An AI agent extracts key details
and maybe the customer's sentiment from an incoming
email. That refined context gets passed along.
Right. Step two, a different AI agent uses, say,
an order number from that context to query your
internal database, pulling up purchase history.
More context. Step three, another agent searches
your help articles based on the issue identified
earlier. Building context layer by layer. Exactly.
Step four. All this refined context, the initial
query, the order history, the relevant help articles,
is then synthesized by maybe a final agent to
draft a highly personalized and accurate reply.
Each step dynamically refines the context for
the next, building a richer and richer understanding.
That's incredibly powerful. And then adaptive
context systems or learning loops. This means
the AI actually learns and gets better over time
from your interaction. Perfectly. This involves
building explicit feedback loops right into your
AI workflows. How does that work? So if an AI
generates a result, users can rate it. Excellent,
acceptable, poor. Simple rating. Yeah. A poor
rating, for instance, could automatically trigger
a process. Maybe it saves that specific prompt
and its corresponding context for a human to
review later. So humans can check the mistakes.
Exactly. This allows you to identify where the
AI stumbled, Make corrections maybe to the knowledge
base or the prompt and essentially train the
system to improve future outputs based on real
-world performance So it learns your preferences.
It learns your preferences your business patterns
and it truly adapts over time This all sounds
incredibly powerful, almost revolutionary. But
is context engineering a magic bullet? What can't
it fix? Yeah, it's not a silver bullet, no. Hallucinations,
while significantly reduced, can still happen,
especially if the underlying data is flawed or
the task is just inherently ambiguous. Garbage
in, garbage out still applies. Fundamentally,
yes. Poorly written, vague prompts will still
yield poor results, even with excellent context.
If the initial instruction isn't clear, context
can only do so much. And, you know, it works
within the basic limitations of the AI model
itself. You can't make a model fundamentally
smarter than its core architecture allows. But
what does it dramatically improve for businesses
using AI? today. Oh, it drastically reduces those
frustrating hallucinations. It dramatically increases
accuracy and personalization in the outputs.
And consistency. And crucially enhances consistency
across all your AI generated content and actions.
So these advanced strategies like chaining and
learning loops, they allow for iterative improvement
and AI adaptation. Yes, making AI systems smarter,
more robust, and much more adaptable over time.
OK, so how does someone actually start implementing
this today without feeling completely overwhelmed?
What's the practical action plan? Right, let's
break it down. The very first step is just to
audit your current AI usage. Look where you're
using it now. Yeah. Where are you consistently
getting those generic responses? Where are you
still manually copy -pasting information just
to get the AI to understand? Identify those specific
pain points first. Then choose your workflow
tool. You mentioned a few earlier. Yeah. For
those just starting out or who like things visual,
no code platforms like Relay .app are fantastic.
Really easy to get started. OK. If your needs
are more complex, maybe require deep integrations
across different business systems. Platforms
like Mindpal offer that kind of enterprise grade
power. And for the more technically inclined
who want maximum control and customization, open
source tools like N8n give you immense flexibility,
though there's a steeper learning curve. And
vitally, start simple. Please, start simple.
Don't try to boil the ocean. Exactly. Pick just
one workflow that addresses a specific, measurable
pain point. Maybe it's those sales follow -up
emails, or internal social media posts, or streamlining
customer support responses. Focus on one thing
first. Yes. Resist the urge to automate everything
at once. Mastery comes from iterative improvement
on small, high -impact processes. Then build
your properly structured, modular knowledge base,
like we discussed. That organized information
is key. Absolutely. Don't underestimate the power
of clear, organized information for the AI. Then
test and iterate, constantly. It's not set and
forget. Definitely not. Adjusting context, refining
your knowledge files, fine -tuning those contextual
prompts. It's an ongoing process. And finally,
scale gradually. OK. Once you have one successful
workflow running smoothly, then add more context
sources, create new workflows, and expand its
reach within your operations. This really feels
like the inevitable direction for AI in business,
doesn't it? Moving from AI is just a helpful
tool to AI as a true team member. I think so.
Context engineering in this light is like the
essential onboarding and ongoing management process
for this new, incredibly powerful team member.
That's a great way to put it. Businesses mastering
this now, they'll undoubtedly have a significant
competitive advantage, wouldn't you say? Oh,
absolutely. The single biggest takeaway, I think,
from all this for our listeners is that context
engineering represents a fundamental shift in
how we work with AI. A shift in thinking. Yes.
It's the key to enabling consistent, high quality
and deeply personalized outputs by creating these
complete intelligent informational environments
for your AI. So starting small with context engineering
is really the path to unlocking AI's full potential
as a true business partner. Exactly. It's the
pathway to deeply integrated and truly understanding
AI systems within your operations. So wrapping
up our big idea today, context engineering is
far more than just another buzzword. Much more.
It's the essential next level evolution for unlocking
AI's true transformative potential within your
business. It's about moving beyond isolated one
-off prompts. To building a rich. dynamic and
continuously evolving informational environment
for your AI. That's the core idea. And by diligently
applying that real framework, making sure your
context is always relevant, efficient. accessible,
and logical. And by properly structuring your
AI knowledge bases with that modular design we
talked about. The Lego blocks. The Lego blocks.
You can transform those generic AI outputs into
high quality, personalized, and remarkably consistent
results. This isn't just about efficiency, though.
It's about gaining a serious competitive edge.
So if you're ready to dive in to put this into
practice, the advice is start simple. Please
do. choose just one process in your business
where you're currently getting those kind of
generic AI responses and apply the real framework
to it. Connect your AI to live data sources,
structure your knowledge files logically, and
then just observe the transformation, see what
happens. The time you invest in learning these
concepts now. It's really going to pay immense
dividends as AI becomes increasingly central
to pretty much every facet of business operations.
It feels inevitable. It really does. It's about
building AI systems that truly understand your
business, your customers, and your goals at a
much deeper level. And for you, our listener,
here's maybe a final thought to mull over. Consider
how treating AI as a true team member, requiring
that proper onboarding and ongoing management
via context, how does that fundamentally change
your approach to automation? What new possibilities
emerge when AI genuinely understands your business
in this deep, integrated way? Something to think
about. That's all for this deep dive. Thanks
for joining us. Thanks for having me. We'll be
back soon with more fascinating insights. Out
of your music.