Original subtitles

Now that you know how to use Claude, let's take a step back and understand what's actually

happening behind the scenes. Don't worry.

This is going to be a simple, plain English explanation, and you don't need any technical

background to follow along.

The goal here is not to dive into complex engineering or algorithms, but to build a

simple mental model of how Claude works.

Once you understand the basics, you'll be able to use Claude much more effectively.

Think of it like driving a car.

You don't need to understand every mechanical detail, but having a general idea of how it

works helps you use it better.

In the same way, Claude might feel like it's thinking or understanding you, but behind

the scenes, it's following a structured process.

In this section, we'll break that process down into simple steps, so you can clearly

see what's going on.

You'll learn what an LLM is, how Claude generates responses, and why your input plays

such an important role in the output you receive.

By the end of this, Claude will feel less like a mysterious black box, and more like

a system you can guide and control.

And that's where the real power comes from.

Understanding how to work with it, not just use it.

Let's start with the most important concept. What is an LLM?

LLM stands for Large Language Model, and at its core, it's a system trained on massive

amounts of text that has learned the patterns of human language.

That idea of patterns is key.

Claude has been exposed to a huge variety of written material, which allows it to recognize

how words, sentences, and ideas are typically structured.

Because of this, it can generate responses that feel natural and human-like.

However, it's important to understand that Claude does not understand language the way humans do.

It doesn't have real experiences, opinions, or awareness.

Instead, it behaves like a very well-read system that has seen countless examples of

how people communicate.

When you ask it a question, it doesn't go and search for an answer.

Instead, it predicts what a good response should look like, based on patterns it has

learned during training.

This predictive ability is what makes it powerful.

It also explains why your input matters so much, because Claude is always working from patterns.

The way you phrase your request directly influences the kind of response it generates.

So if you remember one thing from this slide, it's this.

Claude is not thinking.

It's predicting.

Now let's understand a key difference between Claude and traditional search engines.

When you use a search engine, it looks for existing information.

It scans the internet, finds relevant sources, and gives you links.

Then it's your responsibility to open those links, read through the content, and figure out the answer.

Claude works in a completely different way.

It doesn't search the internet in real time, and it doesn't retrieve pre-existing answers.

Instead, it generates a brand new response every time you ask something.

It does this by predicting what the most appropriate answer should look like, based on patterns

it learned during training.

So instead of finding information, Claude creates it.

This is why it's often said that Claude never Googles anything.

It builds each response from scratch.

This approach has both advantages and limitations.

The advantage is that you get direct, structured, and easy-to-understand responses without needing

to dig through multiple sources.

The limitation is that the quality of the output depends heavily on your input and the

patterns the model has learned.

That's why your role as a user becomes very important.

The better your input, the more useful the generated output will be.

Now let's simplify everything into one core idea, the loop.

Every AI system, including Claude, operates using a simple cycle, input, processing, and output.

This loop is the foundation of how everything works.

First, you provide input.

This could be a question, an instruction, or some background context.

Then comes processing.

This is where Claude analyzes your input, tries to understand your intent, and predicts

the best possible response.

Finally, there is the output, where Claude generates a response and presents it to you.

After that, the loop continues.

You can take the output, refine your input, and keep the conversation going.

This cycle may seem simple, but it's extremely powerful.

The key thing to understand is that everything starts with the input.

If your input is clear and detailed, the processing becomes more accurate, and the

output becomes more useful.

If your input is vague, Claude has to guess your intent, which often leads to less precise responses.

So instead of thinking of Claude as something that magically gives answers, it's better

to think of it as a system that responds to how well you guide it.

The better your input, the better the entire loop works.

Let's now focus on the first step in that loop. Input.

Input is simply what you give to Claude, and it can take different forms.

It might be a question, like asking what machine learning is.

It could be an instruction, such as asking Claude to summarize a document.

Or it could include context, like explaining that you're a teacher and need help creating a quiz.

All of these are examples of input.

The important thing to understand is that the quality of your input directly determines

the quality of the output you receive.

When your input is clear, specific and detailed, Claude has a much better chance of generating

a useful and relevant response.

But when your input is vague or incomplete, Claude has to guess what you want, which often

results in generic answers.

This is why prompt writing is such an important skill.

Your prompt is not just a question, it's a set of instructions that guides the AI.

The more clearly you communicate your intent, the better the results will be.

So instead of writing short or unclear prompts, try to be intentional.

Explain what you want, who it's for, and how you want the response to be structured.

That small shift can make a big difference in how effective Claude becomes for you.

Now let's move to the second step in the loop, processing.

This is where everything happens behind the scenes.

When you send a prompt, Claude begins by carefully analysing every word you've written.

It doesn't skim or skip, it processes the entire input in detail.

After that, it tries to understand your intent.

In other words, it asks, what is the user actually trying to achieve?

Are you asking for an explanation, a summary, a creative response, or something else?

Once it understands that intent, Claude moves to the final part of processing, which is

predicting the best possible response.

And this is where it's important to remember something critical.

Claude is not thinking like a human.

It's not reasoning based on personal experience or real-world understanding.

Instead, it is performing very advanced pattern recognition.

It looks at the patterns it learned during training and predicts what a good response

should look like in this situation.

So while the output may feel intelligent or thoughtful, it's actually the result of highly

sophisticated pattern matching.

Understanding this helps you use Claude better, because you realise that the system depends

entirely on what you give it.

The clearer your input, the easier it is for Claude to process and generate a high-quality response.

Now, let's look at the third step in the loop, output.

This is the part you actually see, the response generated by Claude.

The output is created entirely based on your input and the patterns Claude has learned.

It is designed to be structured, clear, and human-like, which is why it often feels natural to read.

But there's something very important to understand here.

The quality of the output is directly tied to the quality of your input.

If your input includes clear instructions, relevant context, and specific goals, the

output will be much richer and more useful.

On the other hand, if your input is vague or unclear, Claude has to guess what you want,

which usually leads to more generic responses.

So in a way, the output is a reflection of your input.

This is why experienced users focus so much on how they phrase their prompts.

They don't just ask questions, they guide the system.

So when you receive a response, don't just accept it as final.

Review it, refine your input if needed, and continue the interaction.

This back-and-forth process is what allows you to get the best results from Claude.

Now let's talk about tokens, which are the building blocks of how Claude processes language.

As shown on this slide, Claude doesn't read text the way humans do.

We see full sentences and understand meaning instantly, but Claude breaks text into smaller

pieces called tokens.

These tokens can be words, parts of words, punctuation marks, or even special characters.

For example, a simple phrase like, hello world, might be split into multiple tokens, such

as hello, comma, world, and the exclamation mark.

You can think of tokens like puzzle pieces.

Claude takes these small pieces, analyzes them, and assembles them to understand the

overall meaning of your input.

Then it uses the same concept to generate its response, predicting one token at a time

to form a complete answer.

This may sound technical, but the key idea is simple.

Claude processes language in small chunks, not full sentences.

And this is important because it affects how much information Claude can handle at once.

So while you don't need to think about tokens all the time, understanding this concept helps

you better understand how Claude reads and generates text.

Now let's understand why tokens actually matter for you as a user.

As shown on this slide, tokens determine how much information Claude can process in a single conversation.

On average, one token is roughly equal to about four characters of English text, though

this can vary slightly.

Claude has a limit on how many tokens it can handle at once.

This includes both your input and the conversation history.

For example, Claude can handle a very large number of tokens compared to many other tools,

which is why it's so effective for long documents and detailed conversations.

However, there is still a limit.

Once that limit is reached, Claude may lose access to earlier parts of the conversation.

You can think of it like a notepad that eventually runs out of space.

This is important when you're working with long prompts or large files.

The more tokens you use, the more context Claude has to work with, which usually leads

to better responses.

But at the same time, you need to be aware that extremely long interactions may require

you to restate important information.

So tokens are not something you need to manage actively, but understanding their role helps

you use Claude more effectively, especially for complex tasks.

Let's wrap up this section with the most important mental model you should remember.

Claude doesn't know. It predicts.

This idea might seem simple, but it completely changes how you think about using AI.

As shown on this slide, Claude is fundamentally a pattern recognition system.

It has been trained on massive amounts of text and uses that training to predict what

should come next in a response.

Every output it generates starts with your input.

That means the quality, clarity, and structure of your prompt directly influence the result you get.

This is why better prompts lead to better outcomes.

When you provide clear instructions, useful context, and a defined goal, Claude can generate

much more accurate and valuable responses.

On the other hand, if your input is vague, the system has to guess, which leads to weaker results.

So instead of thinking of Claude as something that already knows everything, it's more helpful

to think of it as something that responds to how you guide it.

This puts you in control, and once you understand that, you move from being a passive user to

an active collaborator.

That's the real shift, from using AI to working with AI effectively.