Imagine an AI customer service agent. You know,
it's brilliant at first, handles everything perfectly,
but then, maybe a few weeks in, it starts. Well,
veering off course. Maybe it offers a discount
that just doesn't exist, or it asks you for your
customer ID for the third time in the same chat.
Customers get frustrated, naturally, in your
business. Well, it starts to bleed money. The
core problem here isn't just like a bad prompt
someone wrote. It's something much more subtle.
It's context degradation. Welcome to the deep
dive. Today we're taking a really deep plunge
into context engineering, mastering AI information
flow. And this is just about you know, tweaking
a command here or there. It's really a whole
new way of thinking about AI, how it processes
information, and ultimately how effective it
can truly be. That's exactly right. We're going
to unpack why the traditional way prompt engineering,
while it's still important, it just isn't enough
anymore, not with today's complex AI. And then,
yeah, we'll explore nine really practical strategies,
ways to build what we're calling an informational
nervous system for AI. These are the techniques
that, you know, really lift AI agents up, make
them intelligent and reliable collaborators.
Okay, let's unpack this, and I'm really curious
to understand this fundamental shift you're talking
about. It does feel like the whole landscape
of interacting with AI is changing pretty fast.
We're definitely moving beyond just, you know...
write a good prompt and cross your fingers. Absolutely.
Context engineering, it's not just some fancy
term. It's really about deliberately designing,
managing, and continuously optimizing that stream
of information, what the AI agent gets and what
it remembers over time. Think of it like building
that informational nervous system we mentioned.
It's kind of like overseeing a really complex
conversation, maybe multi -layered, where you
constantly have to track what's been said, what's
crucial to keep, and what's just noise you can
discard. So why now? What's really driving this
shift right now? Why is context suddenly so critical?
Well, there's a great analogy from André Carpathy,
a leading AI researcher. He compares large language
models, these LLMs, that understand and generate
text like humans. He compares them to a new kind
of operating system. And that critical space
where the AI holds all its current info, the
context window, that's its RAM, its limited working
memory, everything the AI needs to think to act
effectively has to fit right in there. Okay,
and just like with our computers, that RAM can
get messy. Really fast. Exactly. And that messiness,
it leads to some pretty significant issues. And
these aren't just technical glitches. They can
become real business headaches. First, you've
got context poisoning. This is when wrong information
gets kind of stuck in the AI's memory. Like,
imagine a sales agent. Just once. It gets hold
of an unofficial 20 % discount figure. If it
saves that nugget, it might just keep offering
that wrong discount. over and over to every customer.
That causes direct revenue loss until someone
catches it. It's pretty insidious. Wow, so a
single bad piece of data can really snowball
like that. It really can. Then there's context
distraction. This happens when there's just too
much irrelevant info. Creates this excessive
cognitive load for the AI. It's like you trying
to listen to five different conversations at
once in a crowded room. The AI gets overwhelmed,
it struggles to focus on its main job, and often
it just defaults to generic kind of off -topic
answers. It can't figure out what's actually
important. And classic one. the needle in a haystack
problem, sometimes called lost in the middle.
Research shows this again and again. LLMs often
ignore crucial details if they're buried in the
middle of a long text. They pay more attention
to the beginning and the end. So your most vital
piece of info could be right there, but the AI
just breezes past it. Like burying the lead,
but for an AI. Precisely. And finally, there's
context drift. So in long -running tasks, the
original goal can just completely fade away.
Picture an agent, right? Its main task is drafting
a comprehensive product launch plan. But maybe
the user keeps interrupting, asking it to check
emails or summarize some unrelated articles.
The context window fills up with these side quests,
and eventually the agent just loses sight of
its main mission, forgets what it was supposed
to be doing. And on top of all these performance
issues, there's a direct economic hit, too, isn't
there? We're talking actual dollars and cents.
Absolutely. Every single token, which is basically
a piece of a word or even a character, that gets
fed into that context window costs API fees.
So an inefficient context management system,
it will quite literally burn your money just
processing junk information. And that adds up
incredibly quickly when you scale. So these aren't
just minor glitches then. They're symptoms of
something deeper. It shows that a perfect prompt
just isn't the whole story anymore, right? Right.
Exactly. It clearly signals that AI needs a much
more sophisticated intelligence system for managing
context if it's going to work effectively over
time. It goes way beyond just a single perfect
command. It's not just about what you tell it,
but what the AI actually hears and remembers
and keeps track of. Okay, so if we need this
intelligence system, what are some of these strategies
for actually building it? Let's start with memory.
That feels pretty foundational. For sure. First
up is short -term memory. This is like fundamental
for any real conversation, right? It lets the
AI remember what just happened, the recent interactions.
Most AI platforms let you configure this pretty
easily. You usually just set a number of recent
messages to keep in mind. It's low latency, meaning
it's fast and pretty simple to set up. So if
a user says, my customer ID is KH8675, the AI
holds onto that for the next question, like,
what's my current balance? just a moment later.
Now, for more granular control, you could use
an external database, something like Postgresql.
That gives you persistence across sessions, plus
you can query it robustly. You can design a really
clear message table structure. That helps a lot
with debugging and analysis later on, though
it does add a little bit more latency. So it's
not just about speed. It's really about the fluidity
of the interaction itself. Without that basic
recall, every AI chat would feel like talking
to someone with severe amnesia. Just repeating
yourself over and over. That's a great way to
put it, yeah. Frustratingly repetitive. Then
there's the idea of more durable intelligence,
which brings us to long -term memory. This is
what keeps critical information handy across
multiple sessions, maybe even ones that are far
apart in time. A simple approach. Maybe just
a text file, or even something like Google Docs.
But more robust systems actually classify memory
types, helps keep things organized. So you might
have user memory for individual preferences or
interaction history like, this user prefers reports
on Monday mornings. Then maybe domain memory
for business rules, product info, stuff like,
our standard return policy is 30 days. And finally,
task memory. This tracks the state of longer
projects like we're currently in the planning
phase of Project X. Okay, but how does an AI
know when to forget things or when information
gets outdated? That seems like a really tricky
challenge for long -term memory especially. Oh,
it absolutely is. That's a critical point. Unlike
us humans, AI doesn't just intuitively forget
stuff that's no longer relevant. It needs periodic
memory validation, active cleanup mechanisms.
Maybe you have an automated process that runs,
say, nightly to check and refresh the agent's
memories. This is a key area for active management.
Otherwise, you risk that context poisoning we
talked about earlier. Right, right. And tools,
they add so much power to AI, but they also seem
like they add another whole layer of complexity
to this context management puzzle. They definitely
do. And that brings us to our third strategy,
context from tool calling. How you describe your
tools to the AI profoundly influences its decisions
about when and how to use them. A really poor
description, like just calling something search
tool, gives the AI very little guidance. It's
vague. But a good description, something like
product information search tool. Use search product.
product name dot string. Use this specific tool
to look up details, pricing, and stop status
for a specific product. See, that's much more
effective. It tells the AI exactly when and how
to use that tool. It's almost like baking the
instruction manual right into its memory. And
when you start chaining tools together, where
one tool's output feeds into the next one context,
it gets incredibly complex, really fast. You
absolutely need strict isolation and summarization
strategies there just to prevent cross contamination
between the steps. So it sounds like AI memory
isn't like ours at all. It needs constant active
gardening, you could say, it doesn't naturally
prune itself. Is that the gist? That's exactly
it. It needs active, intelligent management because
it doesn't inherently filter or forget like we
do. OK, let's shift gears a bit. How do we give
AI access to truly vast amounts of knowledge,
like huge databases or document sets, without
completely overwhelming its limited working memory?
That seems like a massive challenge. Yeah, and
that brings us nicely to our fourth strategy,
ARAG Retrieval Augmented Generation. This is
a really elegant solution, actually. It allows
AI agents to. dynamically pull in information
from large external knowledge bases just when
they need it. Now, to make CAR -RAC work well,
you really need to think about a few key components.
First, your chunking strategy. How you break
up your knowledge base. Don't just split documents
by a fixed size, like every 500 words. Use more
advanced methods, like recursive chunking or
semantic chunking. These try to group related
sentences or ideas together, creating more coherent
text segments for the AI to understand. Next,
choosing and embedding model. This is crucial.
This model is what translates your text into
a sort of mathematical representation that the
AI uses for similarity searches. Picking the
right one is key for finding relevant info. Then,
advanced retrieval techniques. You want to combine
traditional keyword search, like the old -school
BM -25 method, good for finding exact word matches
with semantic search, which understands meaning
and concepts. This combo, often called hybrid
search, usually gives you much more relevant
results, especially for tricky technical terms
or nuanced ideas. Finally, re -ranking. After
you retrieve, say, the top 10 potential documents,
you use a smaller, faster model to re -evaluate
just those top hits. It sorts them again based
on relevance to the specific query, pushing the
absolute most critical information right to the
very top for the main powerful LLM to process
first. Can you give us a quick, concrete example
of that re -ranking? How does that work in practice?
Sure. Let's say someone asks, what's the warranty
policy for the X15 laptop? Hybrid search would
pull documents containing the keyword X15 and
also documents semantically related to warranty
policy. The re -ranker then looks at those results
and says, okay, this specific passage mentions
both X15 and warranty policy very clearly. It
then pushes that exact passage to the number
one spot in the context window. This ensures
the main LLM sees the most relevant snippet immediately.
Gotcha. That makes sense. Now, what about really
complex tasks, things with multiple steps, maybe
over a long period? How do you manage context
there without it just drifting off course completely?
Yeah, for those really complex scenarios, we
turn to strategy number five, context isolation
using a multi -agent approach. For complex tasks,
often the best solution is to build a sort of
hierarchical team of specialized AI agents. Think
of it like a company's org chart. You might have
a coordinator agent acting as the overall manager,
then supervisor agents like team leads, and then
worker agents as the specialists, each focused
on one specific thing. Let's take an automated
marketing team example. A coordinator agent gets
the high -level goal, something like, increase
brand awareness for product Y. It then delegates
parts of this big goal. A content supervisor
agent manages the overall content strategy. And
under that supervisor, you might have a worker
agent research. Its only job is keyword research
and competitor analysis. It deals with all the
messy web data, scraping pages, et cetera. But
crucially, it doesn't pass that mess along. It
processes it and returns only a clean, structured
JSON file with its findings. Then a separate
worker agent writing receives that clean JSON
data. Its context is pure. No HTML noise, no
irrelevant stuff from the web scraping. It just
focuses on writing based on the structured data.
Whoa. Okay. Imagine scaling that kind of setup
to handle like a billion queries. The efficiency
gain could be massive. That's a really powerful
concept. But what are the biggest hurdles in
actually implementing a multi -agent system like
that? Seems complex. Yeah. The primary challenge
is really designing those clear interfaces and
communication protocols between the agents. Each
agent needs to know precisely what information
it expects as input and exactly what format its
output should be in for the next agent down the
line. It takes careful architectural planning
up front to make it work smoothly. So whether
it's breaking down tasks with agents or using
RG, is the core idea basically just giving the
AI only the information it needs for the immediate
task, preventing it from getting overwhelmed
or distracted. Precisely. That's the essence
of it. Deliver only the most relevant pieces
of information right when needed. It prevents
distraction and dramatically improves focus.
Okay. We've covered memory, how AI uses tools,
even setting up AI teams. What else is in the
toolkit for managing this critical information
flow and keeping it clean and effective? Right.
Next up is context summarization. This is all
about the intelligent compression of information.
It's not just shortening, it's making it dense
with meaning. This usually breaks down into two
main types. First, extractive summarization.
This method simply pulls out the most important
sentences directly from the original text. It's
generally fast and pretty factual because it's
just selecting existing phrases. So from a long
email thread, it might pull out, customer reported
error x and we suggested solution y. Straightforward.
Then there's abstractive summarization. Here,
the AI actually generates new sentences to capture
the essence of the content. This often sounds
more natural, more concise. But, and this is
important, it carries a higher risk of hallucination,
where the AI might accidentally make up details
if you don't control it carefully. An abstractive
summary of that same email thread might sound
like. The customer encountered error X and the
team successfully guided them through implementing
solution Y. More fluid, but potentially less
precise if not done well. And for those really
complex workflows that need tight control, predictability,
moving step by step. Ugh. For those, we often
use strategy number seven, context -aware routing
and staging. We actually borrow a concept from
computer science here called a finite state machine,
or FSM. Basically, each defined state in your
workflow, like say, new order or payment pending
or inventory check, has its own clearly defined
context requirements and specific rules for transitioning
to the next state. Think about processing an
online order. The new order state just contains
the basic order details. Agent A validates it.
If it's good, it transitions to the inventory
check state. In that state, Agent B calls the
warehouse API tool with the product ID. If there's
enough stock, it moves to payment pending. If
not, maybe it goes to an out -of -stock state.
This creates simple, predictable steps. It ensures
the AI agent always has exactly the right context
for its current specific task within the larger
workflow. important too. Like, how does it prefer
its data served up? Is there an optimal format?
Absolutely crucial. And that brings us to strategy
number eight. Context formatting. Especially
when you're dealing with structured data coming
from APIs or databases, just feeding it to the
AI as like a natural language sentence is often
really inefficient and surprisingly prone to
errors. Instead, you should provide that data
as clean, well -typed JSON. That's that standard
text -based format for representing structured
data. For example, instead of saying the product
is a cotton t -shirt, it costs 250 ,000 VND,
and they're 50 in stock at warehouse A, you'd
use a JSON. something like product name, cotton
t -shirt, price, 250 ,000, currency, VND, stock,
quantity, 50, location, warehouse A. The LLM
can parse and extract information from that structure
format with much, much higher accuracy. It almost
optimizes its own efficiency of thought when
the data is clean like that. Okay, and finally,
number nine, strategic reduction. How do you
decide what information to cut when the context
gets too big without accidentally losing something
critical? Right, that's context trimming. And
the key is it's not about just randomly chopping
off the end or the beginning. It has to be smart
trimming. One really effective technique here
is to use a cheaper, faster, maybe less powerful
auxiliary AI model to do a kind of relevance
pre -pass. So imagine you have a massive 50 -page
document. Instead of feeding that whole monster
to your powerful, expensive main model, like
GPT -4, you first pass it through a smaller,
faster model, maybe like GPT -3 .5. You prompt
the smaller model, hey, look through this document
and pull out the five paragraphs that are most
relevant to Q4 marketing strategy. Then you take
only those five highly relevant paragraphs and
feed just those to the big GPT -4 model for the
actual analysis or generation task. This technique
can significantly save on API costs while maintaining
really high quality for the final output. I have
to admit, I still wrestle with prompt drift myself
sometimes, so these trimming techniques are absolutely
vital for keeping my own focus and my costs in
check. That's a really clever way to leverage
the different strengths and costs of various
models. OK, looking across all nine strategies,
which ones, in your experience, feel like they
offer the most immediate power or impact for
practical AI development right now? For immediate
impact, especially on efficiency and day -to
-day accuracy. I'd probably say context formatting,
getting that structured data right, and smart
trimming. Those feel like game changers you can
implement pretty quickly. So let's try to bring
this all together. What does this really mean
for us, for anyone building or using AI? What's
the big idea, the main takeaway from diving into
context engineering? Yeah, the big picture is
that context engineering represents a really
fundamental shift in how we approach AI. It's
the transition from being just an AI user, someone
who mainly focuses on writing good prompts, to
becoming more of an AI architect. You're not
just giving instructions anymore, you're actively
designing the entire informational environment,
the flow, the memory, the whole nervous system
of the AI system itself. And these nine strategies
we walk through, they aren't really standalone
formulas you just plug in, are they? They feel
more like building blocks. Exactly. They're building
blocks. They're designed to be combined, customized,
adapted to the specific problem you're trying
to solve. That's how you build truly sophisticated,
robust, and reliable AI solutions. It's about
tackling those immediate, very tangible problems
we discussed, like high costs and accuracy issues
that plague so many AI applications today. And
looking further out, it feels like it's also
about building the necessary foundation for whatever
comes next in AI. Yes. Absolutely. It's laying
the groundwork for the next generation of AI
applications. Those that might be capable of
genuine autonomy, continuous learning from their
environment, and much more intelligent, persistent
interaction with the world. At the end of the
day, the future of truly powerful AI probably
isn't hidden in finding one single perfect prompt,
but rather in constructing a perfectly architected
context system. This has really been a deep dive.
I feel like I have a much clearer map now. I'm
already thinking about how to apply some of these
strategies. Thanks so much for walking us through
this crucial shift. My pleasure. It's definitely
a fascinating area. And hey, here's maybe a final
thought for you for the listeners to chew on.
Think about how designing an AI's memory could
evolve beyond just text. What if it started incorporating
sensory data, like inputs from cameras, microphones,
maybe even touch sensors? What kind of new challenges?
But also, what incredible new possibilities might
that open up for future AI agents? Yeah. Sensory
memory for AI, definitely something to chew on.
Thanks again. And thanks to all of you for joining
us on this deep dive. We'll catch you on the
next one. OETRO music.