We've all been there, haven't we? You're deep
in your code, you've got your AI helper running,
and you ask it something. Maybe refactor a component,
debug a little function, and it just completely
misses the point. Oh yeah. Is your old code,
or it's like, it's got digital amnesia. Forgets
everything you just told it about the project
structure, like five minutes ago. Digital amnesia,
I like that. It's spot on, really. You're in
the zone, things are clicking, and then the AI
just... loses the plot entirely, super frustrating.
Exactly, and that frustration, that constant
need to re -explain things, that's really what
we're diving into today. Our goal is basically
mastering AI context so we can all be more productive,
because the core issue is that lack of... continuous
context. These tools often feel like a blank
slate every time you prompt them. Which waste
so much time. It really does. And it's a known
problem, right? People are thinking about this.
There's even this kind of futuristic idea floating
around called MCP servers, Model Context Protocol
servers. The idea is these servers would let
AIs automatically tap into a project's full context.
Like, really understand. Think of it like a permanent
memory for the AI. Deep understanding. Yeah,
and it's important to say, while these dedicated
MCP servers themselves might still be mostly
concepts, maybe just around the corner, the ideas
driving them, those are incredibly valuable right
now. They give us a sort of roadmap for how we
can... today, teach our current AIs, chat, GPT,
Gemini, Claude, whatever you use, teach them
to grasp our projects on a much deeper level.
Exactly. So that's our mission for this deep
dive. We're going to unpack 10 practical techniques,
all based on those MCP ideas, things you can
actually use now, to give your AI a better memory,
basically, a clearer picture of your project,
which should really boost your productivity,
get the AI working with you, not against you.
So think of this as just a calm, curious look
at how we can make AI truly work for us, moving
beyond those simple one -off questions towards
something more like an informed partnership.
Let's dig in. OK, so first up, something really
fundamental, the project structure itself. This
first idea comes from the concept of a file system
MCP. Imagine an AI server that could just read
and understand your whole project directory.
Know where every file is automatically. Exactly.
So how do we do that now, practically speaking?
Well, it's actually pretty straightforward. Before
you ask the AI to do something involving files,
give it the structure first. You can just run
a tree if you're on Windows or Ellsvoord R on
Mac or Linux. Copy that output. Paste it right
into your prompt. It's like, here are the blueprints.
Gotcha. So you'd say something like? Yeah, you'd
say, here's my project's directory structure.
Then paste the tree output. And then follow up
with, OK, I just moved button .tsx from Cirque
Components over to Cirque Design System Adams.
Based on the structure I gave you, find all the
files and Cirque pages using this button and
rewrite their import paths. Ah, OK. You're giving
it a concrete map. No guesswork. Precisely. So
why is knowing that file location so crucial
for the AI? Well, without that map, it's just
guessing, right? Especially in a big project,
or one that changes a lot. Those guesses about
where files are, that leads to wrong suggestions,
broken code, just misunderstanding what you even
want. Giving it the structure avoids all that
hassle. So basically, it prevents AI from making
assumptions about file paths. That's the core
of it. OK, so that's where things are. What about
how they got there? The history. Good point.
That leads us straight into Git context. This
comes from the Git MCP idea, an AI that knows
your commit history, branches, recent changes,
the whole story of the code. Like it's been pair
programming with you the whole time. Kinda, yeah.
So the practical step here is running commands
like git log or git diff locally, and then just
copying that output into the prompt. Exactly.
You give the AI a clear snapshot of the project's
evolution. OK, so an example might be... You
can say, here's the commit history between tag
v1 .2 .0 and the current main branch. Then paste
the output of, say, git log v1 .2 .0 .head1 line.
Then ask, based only on these commits, please
draft some concise release notes for v1 .3 .0.
Categorize them into new features, bug fixes,
and improvements. Nice. You're giving it the
actual changelog, not just bits of code. So how
does this historical context make the AI a better
collaborator? Well, it shifts it from just generating
code to being more like a knowledgeable teammate.
OK. It sees the why behind changes, the problem
solved, the direction you're heading. Right.
So the code it suggests, or the summaries it
writes, they're just much more relevant because
it gets the intent. It lets AI understand project
evolution and recent changes. Yeah, which is
crucial. Which leads nicely into the next idea,
creating a kind of memory bank. Ah, yes. The
Memory Bank, the concept, the Memory Bank MCP,
was this server that holds all your project decisions,
your coding conventions, architectural rules,
and it remembers them across sessions. Dream
it, right? No more repeating yourself. Definitely
the dream. So how do we mimic that today? You
can use features like custom instructions if
your AI tool supports them. Or, and this works
anywhere, just start every conversation with
a really clear system prompt. Okay. Lay down
the law from the start. Establish the project's
DNA for the AI. So a system prompt might look
like. Yeah, like right at the top. System prompt.
Okay, for this whole chat, stick to these project
rules. One, language is KypeScript. Two, use
Axios for all API calls. Three, React components
are function components with hooks. Clear rule.
Now, help me create a user profile component.
I have to admit, I still wrestle with prompt
drift myself sometimes. Keeping the AI consistently
on track can be genuinely tough. Well, for sure,
it happens. But this memory bank approach, setting
it up, what's the biggest benefit? Does it outweigh
that initial effort? That's a fair question.
It does take a little setup. But think about
how much time you spend correcting the AI later,
right? Re -explaining conventions, fixing code
that doesn't fit the pattern. That drift adds
up. So this memory bank approach, it front loads
that effort, but it pays off over time with consistency.
It ensures consistent AI behavior across all
interactions. Exactly. Consistency is key. And
speaking of keys, knowing how the pieces connect.
Absolutely. Which brings us to analyzing code
relationships. This is inspired by the knowledge
graph memory idea, an AI server that automatically
maps out all the dependencies. Which functions
call which? Which components use others? Like
a big interconnected web. Wow, that would be
powerful. How do we approximate that? We can
use our IDEs, actually, features like find all
references or go to definition. Ah, okay. Use
the tools we already have. Right. Get that dependency
info and then feed it to the AI. So you could
say... You might prompt it like this. Okay, I'm
refactoring the Calculaprice function in utilscalculations
.js. My IDE shows it's used in the components
card .js, page checkout .js, and order service
.js. Based on that, what are the potential risks
here, and what steps should I take to avoid breaking
things during the refactor? Okay, so you're giving
it the dependency map. Why is understanding these
links so important for the AI? Because it lets
the AI predict the ripple effects. If it knows
everywhere that calculate price function is used,
it can warn you before you break something downstream.
Oh, yeah. It can suggest safer ways to refactor.
It shifts it from just coding to being more preventative.
It helps AI foresee impact and avoid breaking
things. Which is always good, less debugging.
What about external info, like docs? Yeah, good
question. Technique five is about retrieving
web and API documentation. This comes from the
Fetching Context 7 MCP ideas. The vision was
an AI that could just pull the latest docs for
any library, automatically. Instant up -to -date
info. Okay, so for now, it's on us. For now,
yeah. Instead of hoping the AI finds the right
docs, or worse, makes something up. Hallucinates.
Exactly. You go to the official docs yourself,
copy the relevant bit, could be text, could be
a code example, and paste that directly into
your prompt. You're curating the info. Okay,
so like... I'm using chart .js version 4 .4.
Here's the official docs snippet for donut charts.
Then you paste the snippet. Right. And then based
on this specific documentation, write the code
for a donut chart showing this data. Sales 60,
marketing 25, dev 15. You're giving it the ground
truth. How does providing that specific curated
info prevent those AI hallucinations? Well, it
directly counters them. You're providing concrete
facts from a trusted source. The AI doesn't have
to guess or rely on possibly outdated training
data. Right. It has the authoritative answer
right there. Dramatically boosts accuracy and
reliability. It provides concrete factual knowledge
for the AI. Makes sense. Less time debugging
the AIs on the app than the creativity. Exactly.
OK, next up, leveraging intelligent search. This
stems from the Tavoli MCP concept, a specialized
search engine for developers. Understood code?
problems. That sounds amazing. So how do we get
close to that now? If you're using an AI with
web browsing built in, like Gemini or the latest
chat GPT, you can guide it search. Don't just
ask a generic question. Tell it how to search.
Instruct it to search, synthesize, and pull answers
from reliable sources only. Oh, OK. Like giving
it research instructions. Pretty much. So you
could say, I'm getting a course. Pre -flight
request did not succeed error with AWS Lambda
and API Gateway. Please search the web, specifically
looking for official AWS documentation and reputable
tech articles on configuring cores for this setup.
Then summarize the top three common solutions
you find. OK, so it's targeted problem solving.
What's the key difference between doing that
and just Googling it myself? The synthesis part
is key. The AI isn't just giving you links. Right.
It's reading multiple sources, figuring out the
common patterns, the most frequent fixes, and
presenting a concise summary. Distilling it down.
Yeah. It's a focused, solution -driven search,
not just information retrieval. It's a focused,
synthesis -driven search, specifically for solutions.
That could save a lot of time sifting through
search results. Definitely. OK, number seven.
Connecting personal notes. Inspired by Obsidian
MCP, the idea of an AI plugging directly into
your personal knowledge base. Your notes, your
requirements. Your brain, basically. Sort of,
yeah. How do we do that, practically? You just
copy and paste. Open your notes app, Obsidian,
Notion, Plain Text, whatever. Find the notes
relevant to what you're working on, copy them,
paste them into the prompt. Give the AI a peek
into your specific thoughts. Exactly. So you
could write, here are my notes on the login page
requirements, and paste your bullet points, email
password fields, standard email validation, password
eight chars, Google sign in button, then ask.
Based only on these requirements, generate the
basic HTML and CSS for this form. How does giving
it these personal notes, which aren't really
code, how does that personalize the AIs help?
It makes the output truly specific to your project,
your thinking. Often, the little details, the
nuances, the specific constraints, they only
live in those notes. Giving the AI that context
helps to generate something that actually matches
your vision, not just a generic template. AI
gains insight into your unique project specifics
and thoughts. That's really powerful for getting
tailored results. Now, what about really complex
tasks? Good transition. Let's talk about applying
sequential thinking. This comes from the sequential
thinking MCP idea, an AI that automatically breaks
down big problems into logical steps, like how
an experienced developer thinks. OK. How do we
encourage that? This one's actually super easy
to apply and really effective. You just explicitly
ask the AI to think step by step or break down
the problem first. Just tell it to slow down
and show its work. Pretty much. It prompts it
to use a more structured approach and actually
explain its reasoning. So an example. You could
say, I need to build a shopping cart feature.
Please think step -by -step and outline a detailed
plan. Include, one, the data structure for the
cart. Two, key API endpoints needed. Add, remove,
update. Three, the main client -side logic. Ah,
okay. Forces it to be methodical. Does asking
it to think step -by -step actually make the
AI, like, smarter? or is something else going
on? It's less about making it intrinsically smarter
and more about forcing it to show its process
and follow a logical path. Okay. It reduces the
chance of it jumping to conclusions or skipping
crucial steps. Right. It guides the AI towards
outputs that are more structured, more reliable,
easier for us to understand and verify. Less
rework. It guides the AI toward more structured,
reliable outputs. That are more trustworthy code.
Makes sense. Okay. Technique 9 deals with something
critical. Handling environment variables securely.
based on the InVault MCP concept, an AI server
that helps access secrets and environment variables
without ever exposing them. Super important.
And the rule today is absolutely critical, right?
Paramount. You never, ever paste API keys, passwords,
any secrets directly into an AI prompt. Full
stop. Never, ever. So what do we do? You instruct
the AI. to write code that retrieves secrets
from environment variables using standard methods,
like process .env in Node or os .getinv in Python.
OK. Tell it how to get the secret securely. Exactly.
So a prompt might be, write a Python function
to call the OpenAI API. Do not put the API key
in the code. Read it from an environment variable
called OpenIAPI key. If that variable isn't set,
the function should raise an error. Perfect.
Keeps the secret safe, out of the code, out of
the prompt. Ooh. Imagine securely managing secrets
for like a billion AI queries automatically,
never risking exposure. That scale is mind -boggling.
It really is. But even on our scale, why is the
security aspect just so vital right now? It's
just fundamental secure coding practice. Hard
coding secrets or accidentally pasting them into
a prompt, that's how major breaches happen. Compromised
accounts, stolen data. So teaching the AI and
ourselves to use environment variables. properly
protects that sensitive info. It prevents those
potentially catastrophic, really costly mistakes.
It's about building secure habits. It protects
sensitive information, preventing costly data
breaches. Absolutely priceless in the long run.
OK, one more. Last one, number 10. Integrating
API contracts. Inspired by APSpec MCP, an AI
that could read, say, an OpenAPI or Swagger file
to know exactly how an API endpoint works. The
precise structure, requests, responses. Exactly.
So practically, we copy the relevant part of
our spec file. That's the idea. Find the specific
endpoint definition in your OpenAPI .json or
Swagger .yaml. Copy that whole definition, the
YAML or JSON block. Paste it directly into the
prompt. Give the AI the undeniable contract for
that endpoint. OK, so, like, I need a JavaScript
function. Here's the OpenATI 3 .0 spec for the
user's user endpoint. Then paste the spec lock.
Right. Then, based strictly on this spec, write
a fetch function called getUserBide that takes
userIde. Make sure it handles the 200 success
response and the 404 error case correctly, as
defined. Got it. How does giving it that explicit
API contract make the interaction more precise.
It just removes all the ambiguity. The AI doesn't
have to guess about parameters or what the response
looks like or which status codes mean what. It
has the blueprint. Exactly. The code it generates
for talking to your API will be perfectly aligned
with your backend. Fewer integration bugs, smoother
development. It gives AI an exact blueprint for
API communication. So that's 10 techniques, 10
ways to give our AI better context, all inspired
by those future -facing MCP server ideas. And
the big idea, the main takeaway here, is pretty
simple, really. You don't need to wait for those
futuristic servers. You can effectively be the
MCP server for your AI. right now. That's it,
exactly. The power isn't in waiting for new tools,
it's in how we use the tools we have. Adopting
that context -first mindset every time you write
a prompt, it changes everything. And when you
start doing this, providing file structure, get
history, that memory bank system prompt, the
API specs, you will see a difference, a really
noticeable difference. The quality of the AI's
output goes way up, and your productivity. It
really does get a significant boost. It's about
having a richer, more informed, and just more
reliable conversation with these tools. And maybe
a final thought to leave you with. What if, by
giving AI this deeper context, we're not just
making the AI more useful, but we're also subtly
changing how we approach problem solving, how
we structure our own thoughts about the code?
That's deep. But yeah, maybe. I'd say just try
one or two of these techniques. Next time you're
working with an AI, pick one, give it a shot,
see how it feels, see what happens. Thank you
for joining us for this deep dive on mastering
AI context. We really hope it helps you build
smarter, build faster, and maybe with a little
less frustration.