You know, when you're talking to a sophisticated
tool, maybe a large chatbot like ChatGPT, it
doesn't just return data. It feels like it's,
well, constructing arguments. It feels like genuine
comprehension almost. Almost human. It really
does, doesn't it? But it's kind of the ultimate
linguistic illusion. The magic isn't actually
understanding, not like we understand things.
It's fundamentally an engine of statistical prediction.
And today we're going to dive into the 10 core
concepts, the essential vocabulary, really, that
make that unbelievably sophisticated prediction
possible. Welcome to the deep dive. Yeah. If
you spend any time in, say, AI meetings or tech
discussions lately, you know the feeling. Jargon
just gets tossed around. It's like technical
confetti. RAG this, attention that, vectorization.
It can be pretty overwhelming. Absolutely. So
our mission today is to cut right through that
noise. We want to give you a clear roadmap. The
10 most critical AI concepts, the ones that really
form the foundation of modern AI engineering.
Think of it like we're building the AI engine
piece by piece. First, the fuel, how it's prepared,
then the actual motor, and finally, how we specialize
it and keep its knowledge fresh. Right. And mastering
these fundamentals, well, lets you move past
the hype. You can start communicating confidently,
make informed decisions. So let's start right
at the bottom, the absolute foundation, the large
language model. The LLM. Okay, the LLM. That's
the big picture, the goal. The entire prediction
system itself. It's a massive, really complex
neural network. And it's trained on just vast
amounts of text data. Its whole purpose, basically,
is to predict the most probable next token in
any sequence you give it. And a token is. That's
the smallest unit the machine actually works
with. Like a word, or maybe even part of a word,
or a punctuation. Exactly right. So if you type
in all that glitters, the LLM predicts is not
gold. Not because it gets the meaning, but because
statistically that sequence is not gold showed
up most often after all that glitters in the
billions of examples it learned from. OK, that's
the core idea. But here's the thing that gets
me. If it only predicts the next token, how does
the output seem so coherent, so logical? How
does just statistics end up feeling like thought?
That's the mind bending part. Coherence is basically
statistical pattern recognition, just scaled
up massively, billions, trillions of times. The
model isn't thinking, not consciously, but it's
recognized patterns that are way too subtle,
too complex for us humans to really track across
all that data. Okay. So before it can even start
predicting, the AI needs to actually take in
the language, right? Which brings us to the first
step in preparing that fuel. Tokenization. Tokenization,
yeah. It's the process of breaking down that
raw input text, whatever you type in, into those
distinct pieces, the tokens. These are the numerical
units the AI can actually compute with. And crucially,
modern AI doesn't just split sentences by spaces.
That's kind of the key insight here, isn't it?
Exactly. That's the old simpler way. Simple splitting
would treat a word like glitters as just one
single thing. But advanced tokenization, sometimes
called subword tokenization, it might break it
down into something like glitz, utters. Ah, okay.
That's structural split. That seems like a really
clever hack. It really is. Because by using these
subwords, the AI can capture patterns that repeat,
like suffixes ling or ellers or ingination. This
lets the model learn about thousands of words
that are built similarly really efficiently,
even if it's never seen that exact word before.
It recognizes the parts. Okay, so we have the
tokens, the smallest pieces. Yeah. But the AI
needs to understand what they mean. Right. Not
just what they look like. And that's where the
numbers come in. You said vectorization. Vectorization.
Yes, this is absolutely crucial. It's the bridge.
It converts those abstract tokens into numerical
representations. We call them vectors or you
can think of them as coordinates within this
incredibly high dimensional mathematical space.
It's literally mapping meaning to math. So you
could almost visualize it like a giant map, a
semantic map. And words like dog, cat, poodle,
maybe rabbit, they'd all be clustered really
close together in that space because they're
conceptually similar. Exactly. Semantic similarity,
how alike things are in meaning, becomes a measurable
mathematical distance. The closer two word vectors
are on this map, the more similar their meaning
and how they're used. So the AI can figure out
that car and automobile are basically the same
concept, even if they never appeared side by
side in its training data. It sees they occupy
similar locations on the map. Oh, OK. So vectorization
turns this. abstract idea of meaning into a physical,
well, a mathematical location, a measurable position
on a map. That's a huge leap. It is. Meaning
gets mapped numerically. So similarity is just
distance, something the algorithm can calculate
and work with. But language is messy. Right.
It's ambiguous. We use the same word for totally
different things all the time. How does the model
know if I say apple, am I talking about the fruit
or the tech company? This seems like a big problem.
And this gets it to attention. Attention. Yes.
This mechanism dynamically figures out that ambiguity.
It's really clever. When the model processes
the word apple, it mathematically weighs, it
pays attention to the words surrounding it in
the sentence. Ah, so it's looking back at the
context it just processed, the words nearby.
Precisely. If Apple shows up near words like
shares or revenue or iPhone, the attention mechanism
gives more weight to those connections, and it
effectively pushes that Apple vector towards
the company cluster of meanings on our map. This
was a huge breakthrough. It came out around 2017.
It's a major reason why modern AI responses feel
so natural and context -aware. It lets the model
kind of read between the lines. Okay, so attention
is like a dynamic focusing lens. It uses the
context of nearby words to resolve that inherent
ambiguity in language. That's a great way to
put it. It contextually focuses to figure out
the intended meaning. Now, thinking historically,
to teach an AI anything, you used to need what's
called supervised learning, which meant like...
Armies of people manually labeling massive amounts
of data. This is a cat. This is not a cat. Super
expensive. Took forever. The scale we see today
with models trained on the whole Internet, that
would have been impossible. Utterly impossible.
That data labeling was a huge bottleneck, and
it was shattered by self -supervised learning,
SSL. With SSL, the AI essentially creates its
own training tasks. It uses the immense amounts
of raw, unlabeled data that's already out there,
like all the text on the web. So the internet
becomes this giant free textbook. And the AI
makes up its own homework questions from it.
Exactly. It takes a sentence, maybe masks out
a word and asks itself, OK, what word most likely
fits here? Or it tries to predict the next sentence
in a paragraph. It uses the inherent structure
of the language itself as the supervision signal.
No humans needed for labeling at that stage.
That shift, SSL, allowing models to learn from
basically the entire Internet without labels.
How critical was that? Was that the key to getting
models like ChatGPT? Oh, absolutely critical.
Foundational even. SSL provided the massive and
crucially cheap data scalability. That's what
let these models grow to the enormous sizes they
are today. Okay, now we should probably clarify
something folks often mix up. The difference
between an LLM and a transformer. Yeah. People
use them interchangeably sometimes. Right. So
the LLM, as you said, is the goal. It's the whole
functioning system that predicts the next token.
The transformer, that's the specific architecture.
The algorithm, the engine design, that makes
the LLM work. Precisely. The transformer architecture
is what's under the hood. It's defined by its
layered structure and its heavy reliance on that
attention mechanism we just talked about. It
basically stacks multiple layers of attention
mechanisms and neural networks on top of each
other. So it's almost like an editing process.
The input goes through the first layer, gets
a basic understanding, then layer two looks at
that output. maybe catches more complex things
like sarcasm or implications between sentences.
That's a good analogy. That stacking is what
gives the model its power. Each layer refines
the understanding built by the previous ones.
It moves from just surface -level word meanings
to understanding deeper relationships and context.
And are all the big, modern, state -of -the -art
LLMs, are they all built using this transformer
architecture now? Pretty much, yes. Right now,
the transformer is the dominant, most powerful,
and most common engine design choice for building
these large language models. Okay. So you have
this incredibly powerful generalist LLM built
with a transformer. It knows about history. Science
can write code. But what if my company needs
an expert on, say, are very specific internal
HR policies or a specialist for analyzing these
medical research papers. The general model probably
won't be perfect. That's where fine tuning comes
in. Exactly. Fine tuning takes that powerful
pre -trained base model, the generalist, and
specializes it. You give it more training, but
this time with highly specific data relevant
to the task. Often it's in the form of question
and answer pairs. You're tailoring its behavior,
its style, its knowledge for a particular domain.
So it's less about teaching it brand new facts
about the world. And more about teaching it how
to act in a specific role, like the right tone,
the right level of detail. That's generally the
main goal. Yes. For instance, if you want a really
helpful customer service AI, you'd fine tune
it by showing it examples of great answers. You
reward it for being direct, empathetic, helpful,
and you penalize it for giving vague or unhelpful
responses. And, you know, full disclosure, I
still wrestle with prompt drifts sometimes trying
to get a general model to consistently stick.
to a specific persona or style without fine tuning.
So that dedicated specialization is often really
essential for reliable performance. Right. So
fine tuning is primarily about shaping behavior,
getting the tone right, drilling down on specific
domain language and style. Consistency is key.
That's it. Behavior and tone are usually more
central than adding vast amounts of new knowledge.
Now, if fine -tuning is like sending the AI to
grad school for specialization, few -shot prompting
is more like giving it quick instructions right
before a task. You include one or maybe a few
examples of exactly what you want right there
in the query itself. Ah, okay. So you're not
retraining the model. You're just showing it
the format or style you want in the moment within
the chat box. Like maybe you provide three examples
of how to cite a source. APA style right before
you ask it your actual research question. Exactly
that. The model sees the pattern in the examples
you provided, the few shots, and it just applies
that pattern immediately to your actual request.
It's super useful for quick things. Ensuring
consistent output formatting, maybe adopting
a specific tone for just one answer, or following
a simple rule without needing a whole retraining
process. So when would you choose one over the
other? When is few shot enough versus needing
full fine tuning? Good question. use few shot
prompting for those quick temporary style adjustments
or format controls things you need right now
choose fine -tuning when you need deep consistent
reliable domain expertise or a very specific
behavioral style that needs to persist across
many many interactions and users okay that makes
sense now probably the biggest practical headache
with standard LLMs The knowledge cut off. The
base model was trained up to a certain date.
It doesn't know about yesterday's news. And critically,
it can't access your private proprietary company
data. Retrieval augmented generation, RAGE, is
the solution here. RAGE is the key, yes. It creates
this really clever, dynamic, three -step pipeline.
First, your query doesn't go straight to the
LLM. It goes to a separate system, a retrieval
system that quickly searches through your own
up -to -date documents, your company knowledge
base, maybe recent reports, whatever is relevant.
It fetches the most relevant snippets from those
documents. Okay, so step one is find relevant
current info from outside the LLM. Then what?
Step two. It takes your original query and combines
it with those retrieved document snippets, the
fresh context. Then step three, that whole package,
query plus context, gets sent to the LLM. Ah,
so you're giving the LLM the answer, at least
the key facts, right before you ask the question.
Pretty much. The model uses that provided verified
external data as its primary context for generating
the answer. The benefits are huge. It overcomes
the knowledge cutoff problem, allows you to use
proprietary info safely, and really importantly,
it significantly reduces the AI's tendency to
just make stuff up to hallucinate because its
answer is grounded in those retrieved facts.
Whoa. Okay, that moment of, that real -time retrieval.
Finding the right snippets from potentially millions
or billions of documents and doing it fast enough
for a conversation. Imagine scaling that to,
I don't know, a billion queries a day across
massive corporate data sets. That's a serious
technical achievement right there. It absolutely
is. It's a phenomenal piece of engineering. It
provides that real -time external verified context.
It grounds the response. It's transformative.
And that intelligent retrieval system you mentioned,
the part within our age that actually fetches
the right. It's usually powered by something
called a vector database, right? This gets around
the limits of just searching for keywords. Exactly.
Traditional keyword search is, well, it's pretty
brittle. If you search your company documents
for refund policy, it's going to completely miss
documents that talk about reimbursement procedures,
even though they mean the same thing. It needs
the exact words. Right. Very literal. So how
does a vector database do better? It changes
the whole game. Remember vectorization. Turning
meaning into map coordinates. A vector database
stores those numerical vector representations
of all your documents. It indexes them based
on their meaning, their location on that semantic
map. So when you make a query, your query also
gets turned into a vector. The database doesn't
search for matching keywords. It searches for
vectors that are close to your query vector on
the map. It searches for semantic meaning, for
conceptual similarity. Okay. So if I search for
something like unhappy customer feedback about
shipping times, the vector database could find
documents talking about delayed deliveries causing
frustration or client dissatisfaction with logistics,
even if the exact words unhappy or shipping aren't
there because the concepts are close on the map.
That's exactly it. It finds things based on conceptual
relevance, not just keyword overlap. It's faster
in many cases, and it's definitely conceptually
smarter. It finds meaning. So if we sort of zoom
back out and connect this all back to that roadmap
we started with, we've actually covered the whole
stack now. Yeah, let's trace it. We started with
the core engine, the large language model, the
LLM, typically built using that powerful transformer
architecture. Then we talked about preparing
the fuel for it, tokenization, breaking down
the language, vectorization, turning it into
meaningful numbers. Attention giving it that
crucial focus to handle ambiguity. Right. Then
we moved to the optimization layer. How do we
make it better for specific tasks? We saw the
two main approaches, fine -tuning for deep, permanent
specialization, like training a medical expert
AI, and few -shot prompting for quick, on -the
-fly guidance on style or format. And finally,
we tackled how to keep that powerful engine updated
and grounded in reality. That's Retrieval Augmented
Generation, RJ. which brings in fresh external
knowledge. And RJ itself relies on the semantic
searching power of the vector database to find
that relevant knowledge quickly. Those 10 concepts,
that's really the core vocabulary of modern AI
engineering. You now have this mental model,
this picture of how all these essential pieces
fit together, how they interact to create these
incredibly complex systems we see everywhere,
from chatbots to scientific discovery tools.
And really, our encouragement to you, the listener,
is to start using this vocabulary. Start thinking
in these terms. Understanding these building
blocks gives you the confidence to cut through
the noise, to participate meaningfully in discussions,
to ask better questions, and ultimately to make
smarter decisions about how AI is used. Yeah,
this knowledge really is the difference between
just being an observer of AI and being someone
who can strategically understand and leverage
it. Okay, so here's a final thought, something
maybe to chew on after this. We've talked about
how these concepts, tokenization, vectorization,
attention, the transformer, let AI master the
complex patterns of human language. But what
happens when we take these exact same pattern
-finding mechanisms, this whole stack, and point
them at completely different kinds of data, not
language? Think about the complex structures
in biology, like protein folding, or the patterns
in financial markets, or material science. Or
even theoretical physics. What new insights might
emerge when this powerful pattern recognition
engine gets applied to domains far beyond just
words? That's something to think about.