99% Of Claude Users STILL Don't Know These 7 Official Prompting Rules

[00:00] Most people learn how to prompt Claude

[00:01] from random tips on the internet. But

[00:03] Anthropic, the company that actually

[00:05] built Claude, released their official

[00:06] prompting guide. Almost no one's read

[00:08] it. I did. And here are the seven most

[00:11] important rules straight from the people

[00:13] who created the model itself. Let's get

[00:15] into it. So, as I mentioned, these tips

[00:17] came directly from Anthropic, the

[00:19] company that made Claude. And this here

[00:20] is the blog post that I'm referring to.

[00:22] So, it walks through a series of tips,

[00:23] not just seven, many more. But I wanted

[00:25] to call out the seven that I feel like

[00:26] are applicable to the broadest audience

[00:28] where people can get direct impact of

[00:30] using these tips. And we'll start with

[00:31] tip one, which is to be clear. Now, this

[00:35] is more critical than ever for all the

[00:37] models that are relevant today, such as

[00:38] Gemini 3, GBT 5.2, Claude Opus 4.5. All

[00:42] of these models are very good at

[00:44] following instructions. So, when you're

[00:46] clear with the model, you're going to

[00:47] get a higher quality response, and the

[00:49] AI is going to work in the direction

[00:50] that you care about. Vague prompts tend

[00:52] to lead to common patterns that are kind

[00:55] of considered AI slop today. So it's

[00:57] generic outputs that aren't very useful.

[00:59] Now why does this happen? It happens

[01:02] because most models have a gravitational

[01:05] pull towards what's most common. So here

[01:08] we have gravity pulling in all the

[01:11] commonalities of the internet. And

[01:12] here's the default state. An example

[01:14] here is UIs. So often times when an AI

[01:17] creates a UI for you, it's going to have

[01:19] purple gradients and it's going to have

[01:20] rounded corners. Reason being is the AI

[01:23] was trained on millions of examples that

[01:24] had that type of aesthetic. So the AI

[01:27] thinks that if it provides you that

[01:28] aesthetic, you're going to be satisfied

[01:30] as a user if you don't provide it very

[01:32] specific instructions. And that's the

[01:34] key here. We have to be specific to

[01:37] escape the gravitational pool that is

[01:39] all those examples the AI was trained on

[01:41] historically. And that's what this tip

[01:42] highlights. So I'm going to walk you

[01:43] through a few examples of what good

[01:44] looks like. So on the left hand side we

[01:46] have a bad prompt. This is weak. So

[01:48] we're basically stating create an

[01:49] analytical dashboard. We we gave no

[01:51] specificity here. A better prompt is

[01:54] create an analytical dashboard. Include

[01:56] as many relevant features and

[01:57] interactions as possible. Go beyond the

[01:59] basics to create a fully featured

[02:00] implementation. And there's a few key

[02:02] things I want to call out here. So first

[02:03] off, we're stating I want you to include

[02:05] as many. So be as inclusive as possible

[02:07] when it comes to the features and

[02:09] interactions relevant to the dashboard

[02:10] we're trying to create. Another thing

[02:12] we're making explicit is the AI needs to

[02:14] go beyond the basics. So, it's going

[02:16] beyond what's normal and creating

[02:18] something that's fully featured that's

[02:20] relevant to the initial dashboard we're

[02:22] trying to create. And this is what a

[02:23] good prompt looks like that's more

[02:24] clear. And here's another example for

[02:26] presentations. So, here we have a week

[02:28] example that says create presentation.

[02:30] An improved version of this is to create

[02:31] a professional presentation in relation

[02:33] to our quarterly results. I want you to

[02:35] include thoughtful design elements,

[02:38] visual hierarchy, and engaging

[02:39] animations where appropriate. And as you

[02:41] can see, we're being very clear on what

[02:42] we want the AI to include in the visuals

[02:44] here, and that's key. So, this is our

[02:46] first tip, which is be clear, quick

[02:48] pause in your regular programming. This

[02:49] video is brought to you by me, as

[02:51] always. So, two quick things. First off,

[02:54] below is a 30-day AI insight series,

[02:56] completely free. You'll get 30 insights

[02:58] in your inbox how you can apply AI to

[02:59] your business and your work. The second

[03:01] thing is, if you'd like to work with me,

[03:02] there's a variety of ways you can do

[03:04] that, either through a private AI

[03:05] community or directly one-on-one. You

[03:07] can check that out below as well. Let's

[03:08] get back into the video. Our second tip

[03:10] is often something that a lot of people

[03:12] skip, which is explain why. And what do

[03:14] we mean by this? Well, when you give the

[03:16] AI some sort of context, so you ask it

[03:18] to do something. If you also add the

[03:20] intent as to why you're asking the AI to

[03:22] do this and why it's important for the

[03:24] AI to take that task on, it can actually

[03:26] infer a lot from what you've stated,

[03:28] even if you've not made it explicit. In

[03:30] other words, Claude can figure out

[03:32] things that you didn't even say as long

[03:33] as you're explicit about why you're

[03:35] asking the AI to do this in the first

[03:36] place. And that often usually deres a

[03:39] higher quality output for you. Now let's

[03:41] look at some examples. So this first

[03:43] example here is a weak example. Not a

[03:45] great prompt. We're saying write this in

[03:46] a formal tone. It can be improved. A

[03:48] better way of saying this is I want you

[03:50] to write this in a formal tone because

[03:52] remember we're emphasizing the why here.

[03:54] It's going to our board of directors and

[03:56] we need to look credible and

[03:57] professional. By providing just this

[04:00] context, the AI is going to be able to

[04:01] create something that's more

[04:02] aesthetically pleasing and aligned with

[04:04] the quality you're seeking by knowing

[04:06] this specific intent. Now, here's

[04:07] another example. So, here we have the

[04:09] weak prompt of saying, "Keep it short."

[04:10] A better version of this is stating,

[04:12] "Keep it short because, again, we're

[04:13] highlighting the Y. I'm sending this to

[04:15] my team via text message, and longer

[04:17] messages don't get read." Again, we're

[04:19] highlighting the Y, which is going to

[04:20] get better quality outputs from the AI.

[04:22] That's our second tip. And now, we'll

[04:24] move on to tip three, which is giving

[04:25] good examples. So the thing with

[04:27] examples, especially in relation to the

[04:29] state-of-the-art models today, such as

[04:30] Opus 4.5, Gemini 3, GPT5.2,

[04:34] all of these models, they follow the

[04:36] examples to the tea. So if you include

[04:37] an example in your system prompt, the AI

[04:39] is going to follow that very

[04:40] specifically. So it's important that you

[04:41] don't misdirect the AI by accidentally

[04:43] including something in your example that

[04:45] you don't want in its output. And a meta

[04:47] lesson in relation to this tip is not

[04:49] only is the example that you explicitly

[04:50] call in the prompt dictating what the AI

[04:52] gives you, but also the way that you

[04:54] write your prompt determines how the AI

[04:56] gives you an output. What does that

[04:57] mean? In the sense that if I write my

[04:59] prompt in a very playful, fun, and

[05:01] simple way, the AI is likely going to

[05:03] respond and give me an output in that

[05:04] way. If I might write my prompt in a

[05:06] very formal and structured way, the AI

[05:08] is going to do the same. So even the way

[05:10] that I write my prompt is going to

[05:12] dictate how the AI outputs its response

[05:14] to me. So it's important to note not

[05:16] just the example but also your prompt

[05:17] matter. Now on to tip four which is

[05:19] asking for the format. Not saying

[05:21] something that you don't want. You want

[05:22] to say what you do want because often

[05:24] when you look at system prompts people

[05:26] have really huge negating terms of

[05:28] saying never do this or don't do that or

[05:30] whatever else. Well with AI today since

[05:32] it follows instructions so well all you

[05:34] have to do is say what you want instead

[05:36] of what you don't want and the AI is

[05:38] likely going to do that more

[05:38] effectively. What are some examples of

[05:40] this? Well here's an example. There's

[05:42] first one is a weak example. So we say

[05:44] do not use markdown in this response.

[05:45] The reason this is weak is because we're

[05:47] using a negating term. So we're saying

[05:48] do not. A better version of this is your

[05:52] response should be composed of smoothly

[05:54] flowing pros and paragraphs. This is

[05:56] going to give us what we want instead of

[05:58] avoiding what we don't want. Another

[05:59] example is here we have a weak version

[06:01] saying make it look nice, not very uh

[06:04] not very clear. And a better version of

[06:06] this is saying use clear headers in each

[06:08] section. Bold the key takeaways. Add a

[06:10] summary at the top. We're very explicit

[06:12] about what we want. The AI is likely

[06:14] going to achieve that because it's very

[06:15] good at following instructions. So

[06:17] that's tip four. Ask for the formats

[06:18] that you want instead of saying what you

[06:20] don't want. Now on to tip five, which is

[06:22] being direct about actions. What does

[06:24] this mean? Well, often times when you

[06:25] want AI to do something for you, we need

[06:27] to avoid suggestive language. So instead

[06:30] of saying suggest, think about,

[06:32] consider, you need to be clear and

[06:34] saying I want you to change this now,

[06:35] edit this thing, or make whatever. by

[06:37] being explicit about it taking action,

[06:40] it will then take the action. But

[06:41] oftentimes the AI can default to being

[06:44] more oriented towards not taking action

[06:46] to make to ensure it's not breaking

[06:48] anything or doing anything it shouldn't.

[06:49] So if you say, "Hey, I want you to

[06:51] suggest this." It will suggest

[06:52] something. It won't change anything. If

[06:54] you want it to change something, then

[06:55] you explicitly state that you want it to

[06:56] change it. So be clear on the action

[06:59] verbs that you're using with the AI. And

[07:01] here are just some examples. So on the

[07:02] left hand side we have a weak prompt

[07:04] which we're stating can you suggest some

[07:06] changes to improve this function. The

[07:07] reason this is weak is we're stating

[07:09] suggest. We're being more careful with

[07:11] our language which means the AI will be

[07:13] careful with its actions. It's not going

[07:14] to do anything. It's only going to

[07:15] suggest what we need to do to the

[07:17] function. This is in relation to code. A

[07:19] more explicit way of doing this that's

[07:21] more actionoriented is saying change

[07:23] this function to improve its

[07:24] performance. That's when the AI goes off

[07:26] and does the action for you. Another

[07:27] example that's unrelated to code is

[07:29] proposal prompts. So a weak version of

[07:31] this is what do you think about this

[07:33] proposal? This is just looking for

[07:34] feedback, no actions. If you want an

[07:36] action to occur from the AI, we would

[07:38] say something that's more strong, which

[07:40] is edit this proposal to make the

[07:41] benefits clearer and add a call to

[07:43] action at the end. This means the AI is

[07:45] going to edit. It's going to do a thing

[07:46] because there is an action verb inside

[07:48] the prompt. So be clear about actions.

[07:50] If you wanted to suggest something or

[07:52] act acts on something, you need to be

[07:54] clear about that. Now, on to tip six,

[07:55] where Claude is actually extremely good

[07:57] at research. It's gotten much better

[07:59] over the last iteration of versions that

[08:01] that Anthropics released with Opus 4.5.

[08:03] Now, let me show you a weak and good

[08:05] example. So, a weak example here for

[08:06] research in relation to AI is saying,

[08:08] "Research my competitors." This is not

[08:10] good because it's vague and it's not

[08:11] clear on what you want. A better version

[08:13] of this is research my top three

[08:15] competitors in the home services

[08:17] industry. For each one, I want you to

[08:18] find their pricing, their main services,

[08:20] and their customer reviews. Compare them

[08:22] to my business and tell me where I have

[08:23] an advantage. So here we're explicit

[08:25] about the type of research we wanted to

[08:27] do, what we wanted to do with the

[08:28] research after it's gotten back and a

[08:30] series of other things. So this is what

[08:31] a good research prompt looks like. And

[08:33] something that's really interesting

[08:34] about the guide that was provided for

[08:36] anthropic is they actually provided a

[08:38] metaprompt. So this specific prompt

[08:39] right here can be applied to almost any

[08:41] research use case that you have and it's

[08:44] going to improve it. I've been using it

[08:45] for the last couple of days and it's

[08:46] night and day in the quality of the

[08:48] research that I'm getting getting back

[08:49] from the AI by using this prompt. So, I

[08:50] highly recommend just copying and

[08:52] pasting this from their guide and using

[08:53] it inside your research. Now, why is

[08:55] this so good? Well, there's a few things

[08:56] as to why this prompt is so useful.

[08:58] First off, we're asking the AI to

[08:59] research in a structured way. So, be

[09:01] more formal in your process. But also,

[09:03] we're asking as you gather data, I want

[09:05] you to develop competing hypotheses. So,

[09:08] multiple opinions against what's

[09:10] happening here. In addition to that,

[09:11] we're also saying, can you uh track your

[09:13] confidence levels over time and adjust

[09:15] them as you learn new things? because

[09:17] you're going to regularly self-critique

[09:19] your confidence levels as well as the

[09:21] hypotheses that you build over your

[09:22] research process. And then we're also

[09:24] noting that the research needs to be

[09:26] broken down from a complex ask to more

[09:29] manageable asks that you then

[09:30] consolidate over time. So there's a lot

[09:32] of really interesting information in

[09:34] this prompt as to why it's useful, but I

[09:35] highly recommend just using this for

[09:37] your research going forward because

[09:38] you're going to get much better

[09:39] responses back for things that truly

[09:40] matter. And then our final tip is

[09:43] Claude's really good at making documents

[09:44] specifically because it uses a claude

[09:46] skill or variety of claude skills that

[09:48] allows it to create documents that are

[09:49] more compelling. So this is going to be

[09:51] in the relation to presentations,

[09:53] animations inside of presentations

[09:54] and/or landing pages like this or any

[09:57] type of visual documents that's relating

[09:58] to a PDF, an Excel sheet, etc. So it's

[10:02] generally better at creating these types

[10:04] of documents and they tend to be

[10:05] formatted more effectively instead of

[10:07] what we used to get back in the day with

[10:08] other models. So what are some examples

[10:10] of this? Well, the first one here is a

[10:11] presentation. So a bad prompt is make me

[10:14] a presentation. A better prompt, a

[10:15] stronger one is create me a professional

[10:17] presentation on topic. You fill in the

[10:19] topic, including thoughtful design

[10:20] elements, visual hierarchy, and engaging

[10:22] animations when appropriate. So we're

[10:24] asking specifically about certain design

[10:26] elements and asking it to think hard

[10:28] about those elements. So when it

[10:29] provides us back a presentation, it's

[10:30] more compelling. So it's a better

[10:31] prompt. Another example here is a

[10:34] report. So instead of saying, "Write me

[10:36] a report." Bad prompt. A better prompt

[10:38] is create a monthly report for my team.

[10:40] Include a summary at the top, sections

[10:42] for each department, charts showing our

[10:44] progress, action items for the ne for

[10:46] the next month, and use clean formatting

[10:48] that's easy to scan. So, we're asking

[10:50] for a lot of things here. We're asking

[10:51] explicitly what should be included in

[10:53] the prompt. We're being very uh specific

[10:55] on the inclusion. We're also being very

[10:57] explicit on the formatting as well

[10:58] because we want be people to be able to

[11:00] scan this instead of having to read it

[11:02] or struggle to read through it. And

[11:03] that's our seventh tip. And as a quick

[11:05] recap, here are just seven rules that I

[11:07] pulled from Anthropic themselves on how

[11:09] you can get most the most out of their

[11:10] models. So the first one here is about

[11:13] being clear. You want to be clear with

[11:14] the model because it's very good at

[11:15] following instructions. So be clear in

[11:17] your prompt. The second thing is always

[11:19] provide your intent. Explain why as to

[11:21] why you're asking the AI to do this

[11:22] thing for you. It can infer more from

[11:24] that and give you a higher quality

[11:25] response. After that, you should give it

[11:27] high quality examples if you decide to

[11:29] give it examples because the AI is going

[11:31] to follow them to the tea. that includes

[11:33] the prompt itself of how you're writing

[11:35] it. So, be very careful on the prompt

[11:37] that you're drafting. After that, if you

[11:39] care about formatting, you want to be

[11:41] explicit about that and ask for certain

[11:42] types of formats from the AI. And that

[11:44] means we're asking the AI what to do

[11:45] instead of what not to do. After this,

[11:47] we have the importance of being direct

[11:49] with the AI. So, if we want it to take

[11:51] action, we need to be clear on it taking

[11:53] that action. If we wanted to suggest

[11:54] something, we need to be clear on that.

[11:56] Also, it's very good at research. And

[11:58] that metaprompt that I showed you, you

[11:59] should use that probably for good on all

[12:01] your research going forward with the

[12:02] specific model because it's night and

[12:03] day compared to what I've gotten in the

[12:05] past. And then finally, it's very good

[12:06] at creating documents and it's good at

[12:08] following different types of formatting

[12:10] um for generalized reports, Excel

[12:12] sheets, presentations, etc. And that's

[12:14] it. So if you enjoyed this, reshare with

[12:15] your friends. And also, as a reminder,

[12:17] two things. First off, below is a 30-day

[12:19] AI insight series, completely free.

[12:21] You'll get 30 insights in your inbox how

[12:22] you can apply AI to your business and

[12:24] your work. Second thing is if you'd like

[12:25] to work with me, there's a variety of

[12:27] ways you can do that either through a

[12:28] private AI community or directly

[12:30] one-on-one. You can check that out below

[12:31] as well. Okay, so now we know that cloud

[12:34] can create great documents. Anthropic

[12:36] even says so. But here's the thing. It

[12:38] still can't match your formatting, your

[12:40] fonts, your colors, your style. You

[12:42] still have to copy the content and fix

[12:44] it by hand in the document. I found one

[12:46] cloud feature that fixes this. Now,

[12:48] every document that comes back to me is

[12:50] exactly how I want it. Let me show you

[12:52] right here. Go ahead, click that video

[12:54] right there.