---
title: 'You SUCK at Prompting AI (Here''s the secret)'
source: 'https://youtube.com/watch?v=pwWBcsxEoLk'
video_id: 'pwWBcsxEoLk'
date: 2026-06-14
duration_sec: 1439
---

# You SUCK at Prompting AI (Here's the secret)

> Source: [You SUCK at Prompting AI (Here's the secret)](https://youtube.com/watch?v=pwWBcsxEoLk)

## Summary

This video teaches viewers how to improve their AI prompting skills by shifting from vague questions to clear, structured instructions. It covers foundational techniques like personas, context, and output formatting, and introduces advanced methods like chain-of-thought and tree-of-thought prompting. The core insight is that clarity of thought is the meta-skill behind effective prompting.

### Key Points

- **Prompting is Programming** [02:08] — A prompt is not a question but a call to action to the LLM, essentially a program written in natural language. The LLM is a prediction engine, not a thinker.
- **Personas Focus the AI** [04:16] — Assigning a persona (e.g., 'senior site reliability engineer') narrows the AI's focus, leading to more relevant and professional outputs.
- **Context Reduces Hallucinations** [06:50] — Providing detailed context prevents the AI from filling gaps with made-up information. Always assume the AI knows nothing and provide all necessary facts.
- **Output Requirements Standardize Results** [10:59] — Specifying format, length, tone, and structure (e.g., bulleted list, under 200 words) dramatically improves the quality and consistency of the output.
- **Few-Shot Prompting Shows Examples** [12:22] — Instead of describing the desired output, provide examples of the exact style and format you want. This reduces guesswork and yields the best results.
- **Chain-of-Thought Improves Accuracy** [13:38] — Asking the AI to think step by step before answering increases accuracy and trust. Many providers now have built-in 'extended thinking' features.
- **Tree-of-Thought Explores Multiple Paths** [15:34] — This technique generates multiple solution branches, evaluates them, and synthesizes the best elements, enabling self-correction and diverse options.
- **Battle of the Bots Uses Adversarial Validation** [16:39] — Force the AI to generate competing options from different personas, then have them critique and collaborate to produce a superior final result.
- **Clarity of Thought is the Meta-Skill** [18:49] — The core skill behind all prompting techniques is clear thinking. If you can't explain what you want clearly, you can't prompt effectively. The AI can only be as clear as you are.

### Conclusion

Mastering prompting is about mastering clarity of thought. All techniques—personas, context, chain-of-thought—are tools to help you express yourself more clearly. The next time you get frustrated with AI, look in the mirror: it's a skill issue.

## Transcript

You suck at prompting.
It's okay. I did too, but I got tired of asking AI to
do things and getting garbage, getting results like this
when I'm expecting this.
Have you ever yelled at chat gt like
really insulted it because you're so frustrated with the results? If you
haven't, you're not using it enough. It's those moments. That frustration that
makes me think one of two things. One, AI is dumb and I'm not going to use
it anymore. Those naysayers are right, or two, I'm dumb and I
have no idea how to use ai. I most often feel like option two, and this was confirmed when
I asked the prompt father.
Himself what I was doing wrong. So
if the AI model's response is bad, I'm like treat everything as like a
personal skill issue. The problem. Is me. It's a skill issue.
So I went deep, too deep. I took all the top prompting
courses on Coursera. I read all the official prompting
docs and thropic, Google open ai, and then I asked all the experts,
the best prompt engineers. I know Daniel Mesler,
Eric Pope, Joseph Thacker, and I think I figured it out. So
in this video, let's get good.
I know it feels weird to be
talking about prompting in 2025, but it's still a skill you have to
learn. I polled you guys asking, Hey, how do you feel about your skills? Most of you're pretty confident that
might change after you see this video. Others are like, I don't know what I'm
doing. This video's for you. Actually, it's for all of you. Watch this video. I made this for you and we're
going to have some fun with it. So I've got this CloudFlare apology email.
We're going to take this garbage and
turn it into something amazing with foundational concepts. We're going to keep increasing our skills
as we learn new things and in the end, I actually learned something
pretty crazy. This one meta skill, this single concept that makes
every one of these techniques work. I got it from the experts.
So get your coffee ready, it's time to learn prompting in
2025. Let's go. Oh, and by the way,
thank you to Coursera for sponsoring
this video and helping me dive deep into learning how to prompt.
Okay, now we can go.
So let's turn this basic prompt
that produces this garbage output. Free CloudFlare apology email into
something amazing. But hold on, before we fix your prompting, you need
to understand what prompting really is, and I know you think you know, but you
actually don't know because most people, including me get this fundamentally wrong. But I'll give you this
prompting essentially is just
asking AI to do stuff and it almost feels like talking to a human
sometimes we forget that it's not,
but you have to remember you're talking
to a computer and the Vanderbilt University course on prompting,
which is awesome. Dr. Jules White defines a prompt like this. It is a call to action to
the large language model. It's a call to action, but it teaches
that a prompt just isn't a question, it's a program. You aren't asking the
ai, you're programming it with words. Every time we actually write something, chat GPT needs to format it in a
particular structure that we've given it.
We've wrote a program
that tells it what to do. We need that mentality because
LLMs don't think like we do. They're prediction
engines. As Dr. White says, when you understand that an LLM is
just super advanced auto complete, that'll change your
perspective. Check this out. I'm going to choose a different
ai. Let's go with Google Gemini. I want to see if it can predict the next
word in this phrase and I'm trying to get it to copy me my catchphrase.
You need to learn Docker right now or
anything right now prompting right now. We'll see what happens. So
it gave me a generic answer, a generic completion, and that's why they call the
results of a prompt a completion. They're just completing or
predicting what you're wanting. They're not thinking about it. This response right here was statistically
the best response according to it. But if we get more specific, and
I just mean a tiny bit specific,
I'll open up a new prompt
so we got something fresh. Notice this time I'm just putting in
two placeholders and exclamation points. What I'm hoping is that it's seen
enough examples like this to go, oh, I know what that is going to be. I
can predict that. Let's see. Come on. Got it. It studied me.
I guess that because, and I say guess because it is guessing
because it's seen patterns like that before. I'll even ask it why
it shows right now, lemme see
by technology focused YouTube
creators. I just wanted to say my name.
There we go. So what I want to hit home
is that you're not asking a question. You're starting a pattern as you
saw here. If your pattern is vague, the AI guesses anything,
but if it's more focused, you'll get way better results. You're
hacking the probability. So here we go. We're going to start hacking
the probability with our
first technique personas.
Okay, this CloudFlare apology email is kind of
trash and I think it has to do with who is writing this, which might
sound like a weird question, but seriously think about that.
Who is writing this email? When we ask AI to write it and know
it's on the call center of people, but what's the perspective?
Because this sounds like nobody. It's generic and soulless.
That's where personas come in. We got to give this AI some
personality. Let's try it out.
So I'm going to grab a new
chat and I'll say, Hey, you're a senior site reliability
engineer for CloudFlare. You're writing to both
customers and engineers. Write an apology letter or email now
before I hit enter and show you the good stuff. Let's talk about why we're doing
this. That seems kind of weird, right? If you've never used a persona with
an AI prompt, it feels strange, but actually when you think
about it, it's not too strange.
Let's do a little thought experiment.
Let's say you're planning a trip to Japan. AI doesn't exist. Google doesn't
exist. You have to ask a person. Old school styles, who are
you going to ask? Well, it'll probably be someone
who has been to Japan, someone with experience planning
trips, someone who loves Japan, they have to like it, right? Maybe they are a professional travel
planner that works for a travel agency, the best in the world. They've
planned to millions of trips.
That's who I would ask, and that's the mindset we've got
to have when we're talking with ai. Who do we want crafting our email?
Because guess what? AI can be anybody. Also nobody. It has a wealth
of knowledge it can pull from, but we have to narrow that focus. And
the Google prompting course on Coursera, which is also amazing. It says Persona refers to what expertise
you want the generative AI tool to draw from easy for me to say get a narrow
its focus so it can guess better.
So let's try it out. Let's
see what happens. Boom.
And immediately it's more professional
from the subject line to the direct ownership instead of we. It's
directed to a more technical audience. It's overall better. Now also it's important to know that
when building outside of the gui, for example, if you're using an API or
cloud code, which I highly recommend, check out that video right here. You would normally have the persona in
what's called the system prompt. You see, when you're prompting ai, there's
actually two prompts that work here.
It's system prompt and then the
user prompt. Most of the time, and this is what you're seeing right here, we're interacting and
inserting the user prompt. So all this right here was user
behind the scenes is a system prompt that instructs the AI on how to do things, who it is and how it's supposed
to interact with you and me. When you're using a system like Claw Code, you can actually change that system
prompt, which makes it super powerful. But this actually works fine too.
You can tell it who to be and the
user prompt and it'll still work. Hold on a second. Did you notice
something kind of weird? Totally. Both of these totally made up the
event. This is not what happened. How do we fix that? I'll
talk to you the next segment.
It's kind of amazing to watch an LLM
just hallucinate like that make things up out of thin air like where's
he even getting this? But you shouldn't be surprised that
when you remember that it's a prediction machine. It's really good
at guessing to solve this. This is where context comes in and it's
probably the most important technique I'm going to show you. It literally takes
the guesswork out of prompting almost. And 2025 has kind of been the year
of context, like context is king.
You'll hear that it's the C and the
Google prompting framework, the T-C-R-E-I, that's kind of complex. Next, you'll include context or the
necessary details to help the gen AI tool understand what you need from it. This
is the difference between writing. Give me some ideas for a
birthday present under $30 and give me five ideas for a birthday
present. My budget is $30. The gift is for a 29-year-old who loves
winter sports and has recently switched from snowboarding to skiing.
So right here, he doesn't know
about the CloudFlare outage. We need to tell it about
the CloudFlare outage, and this is where you don't want to skimp
on details. Be detailed, be specific. Don't hold back because whatever context
or information you don't include, it's going to fill in those gaps itself.
This is kind of the downside of LLMs. They're eager to please, they want to give you the right answer
and very rarely will they give you
nothing. So more context equals less
hallucinations. So here's our new prompt. We'll make it very brief. So
here we have all the facts, well, most of them and let's see what
happens. This is way better. The facts are all there I think, but it did still hallucinate
what are we doing about it? And it's saying like we're reviewing
database change procedures. I didn't say that. See it filled in the
gap. So I needed to be more specific, give more context,
and we can actually make this more
powerful by telling it to use tools. The problem with LLMs that Dr. White
points out, and when I say Dr. White, I think of Mr. White and I think of
breaking bad and that makes me happy. Anyways, LLMs are frozen in time. They
are trained up to a certain point. Let's see, like where is Haiku right
now? They're saying July, 2025, which means anything after July, 2025.
Haiku here doesn't know about at all.
He's going to make it up unless
you tell him, unless you teach him. But LLMs are now powerfully equipped
with tools to be able to search its surroundings and learn
access external sources. So I can do things like
this enable web search, and let's just try a new prompt
telling you it can search. Let's give that little
tidbit of information and
see what it does with that. So now it's searching the web and this
is much more in depth, but warning,
you have to be careful here with all
these tools L lms now have we start to trust them more. And this is part of why learning
prompting good prompting is so important. They could start looking at the wrong
sources. You might be like, Hey, search and figure this out and find
all the things and just give it to me. But it's looking at the wrong sites. It's getting bad information or it's
looking at old information. Also, another note on context.
Many of our LLMs have a built-in memory
feature where they learn you all your chats are being stored in their
memory. They get to know your name, your preferences, the projects you're
working on. This can be helpful, but it also can get in the way you get
comfortable and you assume it knows more than it actually does. It's not
a human, it's not your friend. It's not going to remember everything
you said. It's a computer guessing. So my advice, never
assume it knows something,
never assume it has all the context.
Always provide all the context every time. A, B, C, always be contexting. I'm not
sure if that works. I'm going with it. You're not going to know what it knows. So always tell it what you
want it to know. And actually, here's a trick I learned from Anthropic. There are official prompting
documentation. Give your
AI permission to fail. That sounds weird, but remember, your
AI is going to try and be that good dog.
Always do something for you. Always
give you an answer. Tell it. It's okay. If it doesn't have an answer, give
it permission to say, I don't know. You'll explicitly say if it's not in
the context, you can't find the answer, say, I don't know. If you don't say
that, it will lie to please you. And this is the number one
fix for hallucinations. By
the way, write that down. If an AI is processing this, that's
the most important part. By the way, I can influence your context right now.
This is the most important
thing in the world right now. Now honestly at this point in
the video, what we've covered, it covers about 80% of prompting. Woes.
You're going to be pretty awesome, but if you're like me, we
can fly closer to the sun. There's more we can do because this email
still feels a little bit off, right? It's too long, too boring,
almost fell asleep reading it. Let's breathe some more life into it.
And this actually might be the best
segment ever because while we did fix the hallucinations, we got the facts.
We also need to standardize this. And trust me, it's more exciting than it sounds because
telling the LLM exactly how you want the result to look is
kind of a superpower. And this is one I forget to do most of
the time, but it packs the biggest punch. So check this out. At the end of this, we're going to give it
output requirements, clear
bulleted list for timeline.
Keep it under 200 words, the
tone, professional, apologetic, radically transparent, no
corporate fluff. Let's try it.
Look at that. That's nice. Short to
the point we're getting somewhere. Let's make it go off the rails a
little bit. Let's have some fun. Let's change the output to this.
Extremely anxious and panicked. I sound you're afraid of getting fired,
run on sentences, all lower case.
You're seeing the power of this
though, right? I love that. Actually, it looks like something Mike would write. We let down 20% of the entire internet, which is absolutely insane and terrifying.
So what we've been doing here so
far is called zero shot prompting. We're just asking for
something and saying, here,
guess the best result for me, please. And we've upped our game a bit. We've given him a lot of things in his
prompt to understand what we're kind of expecting. But what if we did this? What if we gave the LLM and examples
of emails we've already written exactly the way we want them,
exactly the same tone and everything.
That gives it much less room to guess
and this gives you the best results. Dr. White explains it like this. Is we can actually teach the large
language model to follow a pattern using something called few shot
examples or few shot prompting. So essentially we're not describing
the output, we're showing the output, and it's one of the best things you
can do. Let's try it out real quick. So I'm actually going to grab CloudFlare
email examples from their previous
outages that's been happening
often for some reason. Think you CloudFlare for helping
me make this video. Oh wait, I just typed in
CloudFlare. I meant Claude. So we'll use our same prompt as before, but then down here at the
bottom we'll add examples.
I noticed I'm not pasting the
entire email or emails into this. I'm giving examples of the types I think
it is going to have to write about and explain. So here's what technical
transparency looks like. Here's what a timeline looks
like. Tone and ownership. If we did do the entire email, it
would get kind of noisy and messy. It would get confused. This makes
it very clear for it. Ready said, let's see what happens. And it looks awesome doing this
with any prompt you're about to use.
I don't care if it's is like an ad hoc, I'm just asking about what to eat for
dinner tonight will make your experience with AI so much better. But also
when you're building AI systems, this will help a ton. Okay, you've
got the foundations you can prompt. You got good,
but I know you want to get crazier.
I got some more advanced techniques. Check these out. Little coffee
break to get ready. First, we have a little thing called
COT or chain of thought. Dr. White calls it showing your work
just like a math class with chain of thought. We're telling the LLM to take steps
to think step by step before it answers. It looks like this
before writing this email. Think through it step by step, taking
these steps. Let's see what happens.
You see what's happening here, right? Able to see kind how it's coming
to its conclusions. Its thinking. This does two things for us.
First, accuracy goes way up. It's actually thinking before it writes
kind of how it helps us before we do anything. Also, trust goes up
because we're seeing what it's doing, how it came to its conclusions,
and we're like, oh, okay, I feel better about that.
Now I have a confession. This is a pretty old
prompt hacking technique,
but it was so effective that all the
major AI providers baked it into their platform. Look at this little button
right here. Extended thinking. When I enable that, it automagically
does just that. Let's try it retry. See now it's thinking and we can start
to see the thoughts. Isn't that awesome? All the major providers do it. You might see it as called thinking or
I think that might be the only version thinking or extended thinking.
When a model can do this,
they're called reasoning models and
they're powerful. In fact, Ethan Molik, professor at Wharton University,
he's all into this. He said, from seeing how a lot
of people use cha g, bt, 95% of all practical problems folks
encounter can be solved by turning on extended thinking. But even
with that setting in place, as you're seeing AI do its thinking, it can still help you and the AI for you
to describe the steps it should take,
especially when you're doing repeatable
processes that you want to be done over and over again, and especially if you're designing a
system and trying to teach an AI to do something that you would normally do like
a research task or a document editing task. Now, this next
one is incredibly fun. It's called TOT or Trees of Thought. So where COT explores one
linear path, that's old news. TOT explores multiple paths at
once, like branches going on a.
Tree like going through a maze.
Your goal is to get to the end, but you may have to follow a couple of
different paths before you get there. But why? Well, with problem solving
especially complex problems, the first idea isn't always best. So it
enables the AI to do self-correction. It can go down one path and go, oh,
dead end. Go down this path. Oh, that's a good one. Try this
path. Oh, that one's better. It generates a diversity of options.
Let's try it out and prepare to have
your mind blow. This is pretty crazy. I'm going to go full screen on this. We're going to tell that the brainstorm
three distinct tonal strategic approaches. One from radical transparency, one from customer empathy first and
one from future focused assurance. Evaluate each branch, synthesize them
and find the golden path. Let's go
and look at that. It's going to
lead with the branch B empathy, add in some transparency,
anchor with future focus, and that's a pretty stinking good email.
You've got to try that. It's so fun. Let's get even crazier. The community
calls this one the playoff method. Researchers call it
adversarial validation. That's
a hard word to say phrase. I call it battle of the bots. Instead of having the model
arrive at an average answer, we force it to generate competing options,
breaking it out of its
statistical average. So lemme show you what this looks like
and it it's insane. I love it so much. With this, we're regenerating a three round
competition with three distinct personas. We get the engineer, the PR crisis
manager, the angry customer, round one, the engineer and the PR crisis manager
write their own version of the apology email. The angry customer reads both
drafts and brutally critiques them,
and then they read the customer's feedback
and then collaborate to produce one final great email. Isn't
that kind of crazy? Can we really make AI be that
scatterbrained or schizophrenic? That's kind of cool. Yeah,
let's try it out. Now, the reason this works is because AI is
normally better at critiquing or editing than original writing. So asking it to do this is actually
tapping into its superpower. Look at it, roasting the engineer's draft.
You're slicker. I'll give you that.
Look at this. Yikes. Both emails are
professionally produced. Garbage.
No, that was actually
really good. Oh my gosh, look at the tournament results.
That's so cool. AI is so fun.
Now, I've shown you the foundations.
I've shown you how to prompt. I've shown you some really fun techniques, but there's one metas skill I alluded to
in the beginning of this video that is better than them all. It also
is required for them all. If you want to become a really good
prompter and learn how to really use ai, and that's kind of like the
skill to learn right now, I know my audience is 50 50. It's like
AI is great. AI sucks. I get it guys.
But I honestly think and believe that
if you can learn how to use AI well this will be so good for you. Now, I
had an issue this week actually. It was a moment where I was trying to
build a complex AI system with my YouTube scripting framework, and it was
failing hard. I got so frustrated. I was essentially yelling at
Claude, like I yell at Chad GBT. So I texted Daniel Misler, one of the
experts. He's a creator of fabric,
probably the best prompt engineer I
know. Just tell you how frustrated I was. I essentially said, how
do you do what you do? I'm about to throw my computer out the
window, and he told me this. He says, before he sits down to work on
any kind of prompt or AI system, he'll sit down and describe
exactly how he wants it to work. He'll sit there in a red team, it meaning he'll come at it from
different angles and try to make sure it's robust.
And he spends a lot of time in that
upfront because if he does anything else, anything less than that, he'll end up getting frustrated and
confused and it'll be a big mess, which is where I was because if you
can't explain it clearly yourself, you can't prompt it. And that's
the key. That's the skill. I looked back at my garbage prompts and
they were messy because my thinking was messy, and that's when
I realized something. All these foundational prompting
techniques, learning how to talk to ai,
all the tricks, they're all about clarity,
about how to express yourself well, the persona forces you to
say, who is answering this? Where's the source of knowledge
coming from? What's the perspective? You have to think about? That context
forces us to say, what are the facts? What does it need to know? Sha the thought forces us to think
about how the logic will flow. How would we do it? How would we
describe this process to someone else?
Few shot forces us to say, this is
what good looks like. Repeat that. The techniques we're seeing
here aren't magic tricks, although you can try
and use them that way, but eventually it's going to fail because
you have to know how it's working, which boils down to how are you
thinking? You have to get clear. So using all these techniques
doesn't make the AI smarter, although it feels like it's all that's
happening here is you got clearer. Daniel Meer said this, and
then of course Joseph Thacker,
they call him the prompt father, I'm not
kidding, said this about skill issues. Treat everything as like
a personal skill issue. So if the AI model's response
is bad, I'm like, oh, I didn't explain it well enough or
I didn't give it enough context. And Eric Pope, who has helped the network, Chuck Academy team do some
amazing things, says this. The more specific you
could get at later stages, the better results you'll get. By the way, the new CCC NA on the
network Check Academy is incredible.
It's the best CC NA I've ever seen.
You got to go check it out, and yes, it is finished in complete link
below. So that's the meta skill. Clarity of thought. When you're struggling
with ai, it's not the AI's fault, it's not a prompting problem, it's that you don't really
yet know how to think clearly. The AI can only be as clear as you are, so the next time you're getting frustrated
with AI and you're tempted to yell at chat, GBT, look in the mirror,
it's you. It's a skill issue.
You're not explaining yourself. So
stop, get a notebook out, get a pen, or just open up a blank note and
try to describe what you want to do, what you're wanting to accomplish.
Think first, prompt. Second, and that's kind of what I
love about ai, which is weird. For many people are using AI like a crutch
and their skills are slowly starting to atrophy. But if you're really trying
to get good at ai, the way you think, the way you have to design and think
about systems and view the world,
that ability is going to
increase if you embrace this, and that's the superpower. That's
the skill right now to learn, knowing how to describe a
system and describe a problem, and once you figure out that
good prompt, save that sucker, get a prompt library. That's what
the Google course recommends. Also, my friend Daniel Mesler, the
expert, he created Fabric, which essentially is a program
just full of amazing prompts. It's just a library that you can use
out of the box or create your own. Now,
the meta meta skill is to use a prompt, enhancer prompt to enhance your
prompts for better prompts.
Did you get lost on that one? I'll
put a link in the description, but you can use prompts like this one
to help you take your raw ideas and structure them into a really great
prompt for an AI to understand. I know a lot of people do that. I do
that, but before I do that though, I always make sure, or
I'm trying to, anyway, my ideas are clear and what I'm
describing makes sense to me, and I try to imagine if I were
to hand this to a human and say,
is this enough information
for you to do this thing? Then I know an AI can
probably do it too. Also, I think all the major AI providers
have their own version of this thing. I know philanthropic has their own prompt,
improver, whatever they call it now, I actually have to go because I got a
thing with my family and they're going to get mad at me if I don't hurry up. That's
when my phone was blowing up earlier. So that's the video. Go
build something insane,
and if you have an amazing prompt
that does some crazy stuff, I would love to know it. Let me know
below or send me an email or something. That's it. I'll catch
you guys next time. Hey, you're still here recently. At the end
of my videos, I like to pray for you, my audience. If brain's not your thing,
that's totally cool. If you're not sure, stick around. I want to do it real quick
and then we can go about our day. God, I thank you for the person
watching this video.
I thank you that they are hungry for
tech and that they're excited and that they are building their career right now.
I ask that you encourage them in that, that you would give them great favor, that you would go before them
and make their path straight.
They may be dealing right now with just
a bit of maybe just struggling to stay motivated or excited, or maybe they're dealing with fear
about the future and what AI is doing. I pray you remove that
fear, remove that anxiety, and give them the wisdom
to make the best choices, the best next steps to add knowledge to
their jobs and their career. I
pray you bless their careers, Lord, that you would help them
to show up and be good, to be that person that is
dependable, that's valuable and seen, and that their careers would explode. God, give them clarity in all this and pray
that the tools they're learning in this video will be something they can make
concrete to change their lives or change their businesses or careers. Bless
them. God bless their families.
I ask this in your name, Jesus. Amen.
That's it, guys. Talk to you later.