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AI Engineering For Beginners in 14 Minutes - Every Major Concept Clearly Explained! | Marina Wyss - AI & Machine Learning | YouTubeToText
YouTube Transcript: AI Engineering For Beginners in 14 Minutes - Every Major Concept Clearly Explained!
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Core Theme
AI engineering focuses on building practical applications using pre-trained foundation models, emphasizing adaptation, deployment, and ongoing management rather than model creation itself.
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There are a lot of really technical
tricky concepts in AI engineering.
Today, we're explaining them in the
absolute simplest way possible, like
you're 5 years old. Okay, realistically,
maybe like 15 years old, but you get the
idea. This video won't get into the
details, obviously. Instead, my goal is
that by the end, you have a highle
intuition for the main topics in AI
engineering today, so you can dig into
more technical tutorials with
confidence. Let's get started. So, what
is AI engineering? AI engineering is the
process of building applications with
readily available foundation models.
We'll talk about foundation models in a
minute. In practice, AI engineers start
from off-the-shelf models, often via an
API, adapt them with prompting, or one
of the other techniques we'll talk
about, and deploy them somewhere for
people to use. They make sure the AI
powered application has proper
evaluation, monitoring, security guard
rails, cost controls, and good enough
performance. This is different from
machine learning engineering which
focuses more on creating and improving
models themselves working with data
training models and optimizing
architectures and metrics. So in the
definition of AI engineering we use the
term foundation model. A foundation
model is a large AI model trained on a
big data set like text, images and
videos from the internet that can be
adapted to many downstream tasks. It's a
generalbased model that you can
customize for specific uses like a
support bot or coding assistant instead
of training from scratch. The foundation
label highlights that these are powerful
building blocks, but still incomplete
without being adapted to a specific
task. This might sound like an LLM, and
that makes sense. Large language models
are a type of foundation model that are
trained to guess the next piece of text
after reading huge amounts of writing.
Because they get very good at these
guesses, they can summarize, answer
questions, translate, and write code.
They don't retrieve facts like a
database. They encode lots of real world
knowledge in their parameters and
generate likely text so they can be
right but also confidently wrong. We've
had text models for a long time but the
main reason modern LLMs perform so well
is because of a change to the way the
model works. Specifically the
introduction of the transformer
architecture. The transformer
architecture is a model design that
allows training to happen in parallel
which makes very large models practical.
Most modern foundation models use this
design. This model design also lets each
word in a sentence pay attention to
other words, not just the ones next to
it. This makes it good at handling long
or tricky sentences and connections like
the dog that chased the cat was brown.
The attention mechanism is how a model
decides which part of the input matters.
Multiple attention heads can focus on
different things at once, like who a
pronoun refers to or the tone of a
sentence. This learning usually means
clearer, more accurate outputs. Speaking
of learning, if you want to go from
understanding these concepts at a high
level to actually building AI
applications, you'll need hands-on
practice. That's where today's sponsor,
Data Camp, comes in. Data Camp has two
excellent AI engineering tracks that
I've been really impressed with. First
is their associate AI engineer for data
scientists track. It's a series of
courses covering everything from machine
learning fundamentals to transformers,
prompt engineering, and fine-tuning. But
what really sets it apart is the MLOps
coverage. You'll learn MLflow, version
control with Git, automated testing, and
CI/CD concepts that most courses
completely skip. For developers, there's
also the associate AI engineer for
developers track. 12 courses plus
projects where you'll actually build
real applications like chat bots,
semantic search engines using OpenAI
API, HuggingFace, Langchain, and Pine
Cone. Again, they include the crucial
deployment and monitoring skills you'll
actually need in production. What I love
about Data Camp is it's all browser
based with interactive coding
environments so you can start practicing
immediately without any setup. Plus,
they have built-in AI helpers to guide
you when you're stuck. Check out Data
Camp using the link in the description
to start building these AI engineering
skills hands-on. Now, back to the
concepts. One thing that comes up a lot
in AI and ML is the idea of a model
learning. When we say a model is
learning, we mean that it's updating its
parameters. Parameters are the internal
numbers that control the model's
behavior. During training, the computer
adjusts these numbers until the model
makes fewer mistakes. Model parameters
can capture more patterns, but they also
cost more to store and run. Model
parameters are the numbers that the
model learns during training.
Hyperparameters are numbers that we set.
One important setting is called
temperature. Think of it like a
creativity dial. Low temperature makes
the model stick to safe, predictable
answers, which is great when you need
accurate facts. High temperature makes
it more creative and surprising, perfect
for brainstorming or writing stories,
but riskier if you need more precise
information. Temperature works with two
other controls called top K and top P.
These limit which words the model can
choose next. Top K says only pick from
the K most likely words. So if K is
five, the model can only choose from its
top five guesses. Top P is a little bit
smarter. It builds a pool of words until
their combined likelihood hits a certain
percentage, like 90%. This pool grows or
shrinks depending on how confident the
model is, giving you a nice balance of
consistency and creativity. Although
technically models aren't actually
returning words, they're returning
tokens. A token is like a small chunk of
text. Sometimes it's a whole word like
cat. Sometimes it's just part of a word
like un from unhappy. And sometimes it's
punctuation. The model reads and writes
one token at a time. Kind of like how
you might read letter by letter when
you're first learning to read. When
people talk about how long a model's
memory is, they're counting tokens, not
words. Speaking of memory, model context
is basically how much text the model can
remember and work with at once. Think of
it like your working memory when reading
a book. You can keep track of the
current chapter and maybe the last few
chapters, but not the entire book series
at once. Context includes your
conversation history, the prompt, any
documents you've shared, and the
response being generated. Models have
context limits. When you hit that limit,
the model starts forgetting the oldest
parts to make room for new information.
This is why very long conversations
sometimes lose track of things you said
at the beginning. When we chat with a
model, we are prompting the model,
right? And the prompt makes a big
difference for the kind of output we
get. Prompt engineering is a fancy way
of saying writing really good
instructions. You can tell the model
what role to play, like you are a
helpful teacher, what format you want,
like give me bullet points, and what
rules to follow, like always include
your sources. Good prompts are like
giving clear directions. They help the
model understand what you want and give
more consistent results. There are
actually two types of prompts. The
system prompt is like the house rules.
It sets the model's default behavior and
stays the same for every conversation.
The user prompt is your specific request
right now. Think of the system prompt as
telling someone you're a professional
email writer and the user prompt as
write an email declining this meeting.
Sometimes you don't need to give
examples in your prompt. You just ask
the model to do something and it figures
it out. That's called zeroot learning.
Like asking translate this to Spanish
without showing any examples first. But
often showing a few examples works way
better. That's fshot learning. You
include a handful of examples in your
prompt to show the exact style you want.
The model then copies your pattern for
your new request. This is all part of
something called in context learning.
You're not permanently changing the
model. You're just teaching it
temporarily within one conversation by
including examples right in your
message. If you want more permanent
changes, you can use fine-tuning. This
actually retrains the model on your own
examples, so it consistently behaves a
certain way. Unlike prompting, this time
you're actually changing the model's
internal parameters. It's useful for
specialized language like medical or
legal writing or getting a very specific
tone or output format. It costs more
time and money than prompting, but
you'll probably get more reliable
results. Full fine-tuning can be
expensive, so there's a shortcut we use
called PFT, parameter efficient
fine-tuning. Instead of changing the
entire model, you just add a small
adapter layer on top. It's like editing
just part of a document versus rewriting
the entire document from scratch. You
get most of the benefits of fine-tuning
with way less compute and storage. Laura
is an example of PEFT. Two other ways to
make models more practical are
quantization and distillation.
Quantization is like compressing a
highresolution photo. You store the
model's numbers with fewer bits, which
makes it smaller and faster while
keeping most of the quality.
Distillation is different. In this case,
you train a smaller student model to
copy a larger teacher model. The student
learns from both the teacher's answers
and how confident it is about them. You
end up with a faster, lighter model that
keeps much of the original's knowledge
and capabilities. Most consumer AI
models go through one more step called
preference fine-tuning. Humans rate
different model responses, and the model
learns to prefer answers that people
like. This pushes it toward being more
helpful, safe, and polite rather than
just technically correct. It's the
difference between a model that can
write and one that writes in a way
humans actually want to read. or the
difference between a sickopantic model
and one that is more down to earth. Now,
even the best models sometimes make
stuff up or don't know recent
information. That's where Rag comes in.
Retrieval augmented generation. Instead
of just relying on what the model
memorized during training, it first
looks up relevant documents maybe from a
database, then writes its answer using
that fresh information. This reduces
madeup facts and keeps answers current
without having to retrain the whole
model. Rag depends on something called
embeddings. An embedding turns text into
a list of numbers where similar meanings
end up close together mathematically. So
car and automobile would have very
similar lists of numbers even though
they're very different words. This lets
you search by meaning, not just exact
word matches. These embeddings get
stored in a vector database, which is
basically a database that's really good
at quickly finding the most similar
embeddings to your question. When you
ask something, the system finds the
closest matching documents and feeds
them to the model along with your
question. Before documents go into the
vector database, they get split up
through chunking. You can't stuff an
entire book into the model at once
because of the context limit. So, you
break it into smaller pieces that are
easier to search and process. If you go
too big, you get a lot of irrelevant
stuff. If you go too small, you lose
this important context. After finding
relevant chunks, ranking puts them in
order of usefulness. The best evidence
goes to the top, which leads to shorter,
clearer, and more accurate answers.
Under the hood, different parts of these
systems use encoders and decoders. An
encoder turns text into those compact
numeric summaries that capture meaning,
perfect for search and understanding. A
decoder turns summaries back into human
text, one token at a time. Great for
generating responses. Some models only
encode, some only decode, and some do
both. So far, we've talked about models
that just chat, but agents can actually
do things. An agent is like an AI
assistant that can plan steps and take
actions to reach a goal. It might search
the web, read the results, do some
calculations, write up an answer, and
send it back to you. Agents can use
memory from past conversations, retry
when something fails, and adjust their
plans. Agents work by calling tools.
External abilities like web search,
calculators, code runners, email,
calendars, and databases. Tools let the
model move from just words to actual
actions. But how do agents actually use
tools? It's actually pretty simple. When
you build an agent, you give it access
to functions it can call. For example,
you might have a search web query
function or a send to subject body
function or a send email function. The
agent doesn't run this code directly.
Instead, it generates a special message
saying, "I want to call search web with
the query weather in Paris." And your
application code actually makes that
function call, then feeds the results
back to the agent. All of this leads to
inference, the moment when you actually
run the trained model to get an answer.
The model generates one token at a time
guided by your temperature and top K or
top P settings. Cost depends on how many
tokens you process and how big the model
is. There are two main ways to do
inference. Online inference serves
answers in real time to live users like
chat GPT responding as you type. It
needs to be fast and handle traffic
spikes smoothly. Batch inference
processes lots of items offline like
overnight jobs. You trade instant
responses for higher throughput and
lower costs. It's perfect for things
like classifying millions of reviews or
summarizing archives. For online
systems, latency matters a lot. Latency
is the delay between asking a question
and getting the first useful output.
Users definitely notice the difference
between 200 milliseconds and 2 seconds.
Streaming helps by showing partial
results immediately instead of waiting
for the complete answer. So, we can
evaluate how good our system is by how
fast it returns results. But how do we
know if the actual models we're using
are any good? One way is with model
benchmarks. Benchmarks are like
standardized tests that compare models
on skills like math, coding, reading,
comprehension, and safety. They're
helpful for tracking progress, but
they're not the whole story. Real world
performance, and human feedback still
matter most. When researchers want to
measure model performance more
precisely, they use specific metrics.
Here are three of the most common ones
in AI engineering. Perplexity, blue, and
rouge. Perplexity measures how surprised
a model is by text it hasn't seen
before. Lower perplexity means the model
predicted the text better. It's less
confused. Think of it like a reading
comprehension test. If you can predict
what comes next in a story, you probably
understood it well. Blue and rouge are
used for tasks like translation and
summarization. Blue compares a model's
output to reference correct answers by
counting matching words and phrases. If
a translation shares lots of words with
a professional human translation, it
gets a high blue score. Rouge is
similar, but focuses on recall. How much
of the important content did the model
capture? It's especially useful for
summarization. If a model's summary
includes most of the key points from the
original text, it gets a high rouge
score. These metrics aren't perfect. A
translation could have all the right
words but still sound weird, or a
summary could hit all the keywords but
miss the main point. But they give
researchers a quick automated way to
compare different models. Another way to
tell how good the model is is to use one
model to grade another model's answers.
An LLM as judge scores responses against
rules like followed instructions, used
reliable sources, or clear writing. It's
much faster and cheaper than using only
human reviewers. Though it's not
perfect, so spot checks are important.
Finally, as AI systems get more complex,
we need better ways for different parts
to work together. MCP or model context
protocol is like a universal adapter
that lets models, apps, and tools
connect through the same interface.
Instead of building custom integrations
for everything, you just plug into the
standard. It's about making AI systems
that actually work together instead of
being isolated little islands. And that
wraps up our list of the main topics in
AI engineering at a high level. I have a
comprehensive 76-inute crash course that
goes into way more detail, so check that
out next. Thank you so much for watching
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