Original subtitles

Let's now move into one of the most practical and eye-opening parts of this course,

understanding the difference between bad and good prompts.

You can use the exact same AI tool, but depending on the prompt,

you can get completely different results.

This is something that surprises many beginners.

They assume that if the AI is powerful, it should always produce high-quality answers.

But in reality, the AI is only as effective as the input it receives.

When prompts fail, it is not random, it is predictable.

Weak prompts lead to weak outputs.

In this section, we are going to break that down clearly.

You will see why some prompts produce generic, unclear, or unusable results,

and more importantly, how to fix them.

This is where things become very practical,

because instead of just learning theory,

you will see real examples of bad prompts,

and how they are transformed into strong ones.

Once you understand that transformation,

you can apply it immediately to your own work.

The goal here is simple,

to show you that better results do not require a better AI.

They require better prompts.

And once you see the difference side by side,

it becomes very clear what needs to change.

Now let's address a very common misconception.

The real problem is not the AI, it is the prompt.

Most users write weak prompts,

and then blame the tool when the results are not useful.

They receive outputs that feel generic, unclear, or difficult to apply,

and they assume the AI is not capable enough.

But if you examine the situation closely,

the issue is almost always the input.

Weak prompts lack direction.

They do not clearly define the topic,

they do not specify the audience,

and they do not explain how the output should be structured.

Because of this, the AI has to fill in the gaps.

When it does that,

it defaults to the most general and average response possible.

This is why the outputs feel generic.

They may be correct, but they are not useful.

They do not align with your specific goal.

As a result, you end up spending more time editing

and fixing the output instead of using it directly.

The key takeaway here is simple.

If the output feels generic, the input was probably generic.

Once you understand this, your approach changes.

Instead of blaming the AI, you focus on improving the prompt,

and that is where real improvement begins.

Now let's understand what makes a prompt bad.

Bad prompts typically share a few common characteristics.

They are vague, lack context, have no structure,

and do not clearly define a goal.

A vague prompt does not specify the topic clearly.

A prompt without context does not explain

who the content is for or why it is needed.

A prompt without structure does not tell the AI

how to organize the response.

And a prompt without a clear goal

leaves the AI guessing what you actually want.

When these elements are missing,

the AI has no choice but to make assumptions.

And when it has to guess,

it defaults to the most average and generic answer possible.

This results in outputs that are broad instead of targeted.

They may sound correct, but they are not particularly useful.

Because of this, you end up spending time editing the response

instead of using it directly.

This is why the idea that vague input leads to vague output

is so important.

It happens consistently.

So instead of expecting the AI to fill in the gaps,

your goal should be to remove those gaps completely.

When you do that, the quality of the output improves instantly.

Let's now look at a real example of a bad prompt. The prompt,

Tell me about marketing may seem reasonable at first,

but it has several issues.

First, it is too broad.

Marketing is a very large field

that includes many areas such as digital marketing,

branding, advertising, and strategy.

The prompt does not specify which area to focus on.

Second, there is no direction.

It is unclear whether the response should include strategies,

tools, trends, or definitions.

Third, there is no audience.

The AI does not know whether the explanation is meant for a student,

a business owner, or an expert.

Each of these would require a different type of response.

Because none of this information is provided,

the AI has to guess.

As a result, it produces a generic, textbook-style answer

that is not particularly useful.

This is exactly what weak prompts do.

They create outputs that may be correct,

but are not practical or actionable.

That is why this type of prompt is ineffective in real-world scenarios.

Now let's look at how to fix that same prompt.

The improved version is,

Explain digital marketing strategies for small businesses

in five bullet points with examples.

This version is much more effective because it removes ambiguity.

It specifies the topic by focusing on digital marketing,

instead of marketing in general.

It defines the audience by targeting small businesses,

which helps the AI tailor the response.

It also provides a format by requesting five bullet points with examples,

making the output structured and easy to use.

With just a few adjustments, the prompt becomes clear and purposeful.

As a result, the output becomes focused, actionable,

and ready to use without much editing.

This example clearly shows the power of improving your prompt.

You are not changing the AI.

You are changing the input.

And that single change transforms the quality of the result.

So whenever you feel that the output is not useful,

do not start over completely.

Instead, refine the prompt by adding clarity,

defining the audience, and specifying the format.

Even small improvements can lead to significantly better outcomes.

Now let's look at another example of a bad prompt

to further reinforce this idea.

The prompt, write an email, is extremely weak.

It contains only three words and provides almost no useful information.

There is no purpose defined.

Claude does not know whether the email is for sales,

a follow-up, a complaint, or a request.

There is no tone specified.

It could be formal, casual, urgent, or friendly.

There is also no context.

Claude does not know who the email is being sent to,

what the relationship is, or what the goal of the message is.

Because all of these elements are missing,

the AI has nothing meaningful to work with.

As a result, it generates a generic template

that is unlikely to be useful in a real situation.

This is a perfect example of how weak prompts force the AI to guess,

and guessing leads to average, unfocused results.

It is not that the AI cannot write a good email.

It is that it has not been given enough direction to do so.

This highlights an important lesson.

The more information you provide, the better the output becomes.

Now, let's look at the improved version of that same prompt.

Instead of saying, write an email, a better prompt would be,

write a professional email to my manager

requesting two days of leave in a polite tone.

This version is significantly stronger because it provides clear direction.

The task is defined as writing an email,

but now it includes a specific purpose, requesting leave.

The context is also clear,

as it specifies that the email is to a manager,

which influences the level of formality.

Additionally, the tone is defined as polite,

which shapes the language and structure of the response.

With these details included, Claude no longer has to guess.

It can generate a response that is focused,

appropriate and ready to use with little or no editing.

This example shows how even a small improvement in your prompt

can lead to a dramatically better result.

You are not adding complexity, you are adding clarity.

And that clarity is what transforms the output from generic to practical.

Now, let's understand how to fix weak prompts in general.

The process is actually very simple.

In most cases, you only need to add a few key elements

to transform a weak prompt into a strong one.

Start by making the topic more specific,

so that the AI knows exactly what to focus on.

Then add context by explaining who the content is for or why it is needed.

Finally, include structure by defining how you want the output to be presented,

such as bullet points, a summary or a formal message.

These small additions make a huge difference.

They remove ambiguity, reduce guesswork

and guide the AI toward a more useful response.

You can also think of this as applying the formula you learned earlier,

role, task, context and format.

You do not always need all four elements,

but adding even one or two can significantly improve the output.

The key idea is that you do not need to rewrite your prompt completely.

You simply need to improve it step by step.

Now, let's talk about the mindset you should adopt when working with prompts.

Many people think they need to start over

if their first attempt does not produce a good result.

But that is not necessary.

Instead, you should focus on improving your existing prompt.

Start with a simple version, then gradually add clarity and structure.

For example, if your initial prompt is too vague,

you can refine it by adding more detail,

specifying the audience or defining the format.

Each small improvement brings you closer to the result you want.

This is what makes prompting an iterative process.

You are not aiming for perfection in the first attempt.

Instead, you are improving step by step.

This approach saves time and makes the process more efficient.

Over time, you will also get better at writing strong prompts from the beginning.

So instead of thinking in terms of right or wrong prompts,

think in terms of improving prompts.

That shift in mindset makes a big difference in how effectively you use AI.

Let's wrap up this section with a clear and powerful takeaway.

Fix the prompt to fix the output.

This idea summarizes everything you have learned in this section.

Bad prompts lead to vague, generic, and time-consuming outputs.

Good prompts lead to specific, powerful, and ready-to-use results.

The difference is not in the AI.

It is in how you communicate with it.

The quality of your output is directly determined by the quality of your prompt.

So instead of blaming the tool when something does not work,

focus on improving your input.

Add clarity, define your goal, include context, and structure your request.

These small changes can completely transform the results you get.

As you continue practicing, this process will become more natural.

You will start to recognize weak prompts immediately and know how to fix them.

And once you reach that point, you unlock the true power of AI.

Because you are no longer guessing, you are directing.