So if you've ever talked to an AI like Claude
and you've asked for something deep, something
professional, but what you get back just feels,
I don't know, generic. A little flat. Yeah, flat
or even just wrong. It can feel like you've just
hit a brick wall. That frustration is so common.
But what we're learning is that the wall isn't
really the model's power. It's the quality of
the conversation we're starting. We're whispering
at it. Exactly. We're giving these vague instructions
to this massive system. And then we act surprised
when... The output is just, you know, high -tech
mush. Well, that ends today. We know that you,
the learner, are always looking for that shortcut,
that efficient path to getting high -value knowledge
and mastering AI communication. That is the path.
It's the ultimate shortcut. So today we're doing
a deep dive into the playbook. We've taken Anthropic's
own guidance, their 10 golden rules for crafting
really effective prompts for models like Claude
Opus and Sonnet. Our mission today is to move
you from just being a passive user of AI to becoming
a precision orchestrator. Yeah, and we're going
to structure this as a clear path. We've got
three pillars, okay? First, the foundational
rules for control and intent. Second, how to
structure execution for big projects. And then
finally, the advanced tricks that the top 1 %
are using. Before we jump in, there's one term
we should probably define. Right. Few -shot prompting.
What is that? It's actually really simple. It
just means giving the AI examples. You show Claude
a sample of the perfect output you want, the
tone, the format, and you just tell it, do this.
Got it. Mimic this exactly. Exactly. Okay, so
let's start with that foundation. Rule number
one is to be, and I like this wording. painfully,
obsessively specific. Painfully is the key word.
This isn't just a good habit. It's about understanding
how literal these machines are. If you leave
any room for ambiguity, the AI has to guess.
And guessing is where hallucinations start. It's
where you get bad output. Quad is perfectly,
almost unnervingly literal. You have to close
every single loophole. The analogy they use is
great. Don't just ask for a pizza. Right. You'll
get who knows what. You have to say a large,
thin crust pepperoni pizza with extra cheese
cut into squares. And when you apply that same
logic. to high stakes work, that's where the
power is. Don't just say write a Python function.
Specify everything. Write a Python function for
a data analyst audience. Make sure all the doc
strings follow PEP8 standards. And I need three
specific examples for edge cases. There's no
room for randomness there. That leads right into
rule two, which is tell the AI why you want it.
The intent principle. And this is transformative.
A command tells the AI what to do. Intent tells
it why. And that turns a command into a mission.
Oh, okay. The why is the secret sauce. If I just
say, summarize this legal document, you might
focus on everything equally, the footnotes, the
header. Right. But if I say, summarize this for
a non -lawyer executive who needs to grasp key
risks before a meeting in one hour, now the AI
knows. Now it knows that anything that isn't
a key risk is just noise. Exactly. It changes
its entire approach. It becomes a specialized
risk analyst, not just a summarizer. And that
priority shift is where rule three comes in.
Your examples are the unquestionable truth. This
is that few shot prompting inaction. When you
give Claude a sample, it treats it like sacred
text. It will mimic that style, that structure,
with total fidelity. That's so powerful. You
don't have to try and describe an abstract tone
like professional but approachable. No, you just
show it. You just give it an example of what
that looks like. Yeah, the best practice is to
keep a folder of your own best work, your best
emails, your best reports. And when you need
something new, you just say, here's the tone
I need. Now apply it to this new request. It's
the fastest way to clone your own voice. So by
supplying that intent, how does the model fundamentally
change its approach? Intent lets it filter content.
It prioritizes the important stuff like risks
over the low -value stuff like jargon. Okay,
so if those first three rules are about defining
the mission, what happens when you have a long,
complex task? Right. Now we're shifting from
being a basic user to someone who actually structures
the entire workflow. This is where you become
the power user. Exactly. And it starts with rule
four. Big projects need small, strategic checkpoints.
Oh, this is a big one. Trying to get a 50 -page
report in one shot. It's such a common mistake.
It just overwhelms the model. And the result
is almost always this incoherent blob where the
beginning has no idea what the end is doing.
I've done this. I tried to write a 10 -page market
analysis and it spent, I don't know, 40 % of
its time on this huge, irrelevant intro about
the history of market analysis. That's such a
relatable failure. Yeah. The fix is to become
the project manager. You force what's called
a chain of thought approach. So step by step.
Step by step. First, just generate the outline.
That's it. You approve it. Then you say, okay,
now write the introduction based only on that
outline. Then chapter one, you course correct
at every step. That way you catch a fatal flaw
early instead of after an hour of wasted generation.
Precisely. Which brings us to rule five, which
is about tone. Demand action, not advice. Be
direct. Be very direct. Use an optive command.
You're pushing it into execution mode, so you
want to avoid all that softening language. But
what if you're using it for, say, client communication?
You don't want to sound abrasive. The key is
that the command needs to be clear, not rude.
The passive way is asking, what do you think
of this Python code? The active, precise way
is, refactor this code to improve performance
and readability. Return only the refactored code.
The command itself dictates the format and the
goal. No fluff needed. Right. Clarity is kindness
to the AI, which leads to rule sex, specifying
the what, not the whatnot. Controlling the format.
This is so critical for automation. Telling an
AI what not to do is actually really vague. Phrases
like don't use markdown or don't use bullet points
get ignored all the time. You know, this is my
vulnerable admission for the day. I still wrestle
with this. I use negative commands and it just
seems to ignore them sometimes. It spits out
the bullet points anyway. And that's exactly
why. The model prioritizes the positive act of
generating text over remembering a negative constraint.
So the fix is positive instruction. Always. Instead
of what to avoid, define the exact structured
output you want. For instance, output the final
answer as a single JSON object with two keys,
summary and key points, which must be an array
of strings. That is unambiguous. For the learner,
what is the biggest risk of using a negative
command like don't use X? They're vague. The
model often ignores them and does the exact thing
you want it to avoid. So we've got the foundation.
We've got the structure. These last four rules,
these are the advanced techniques. Yeah, this
is where the magic happens. This is what the
expert prompters are doing. Okay, what's rule
seven? Rule seven is to use simple labels, specifically
XML tags, to steer behavior. Most people just
throw everything into the prompt. The instructions,
the context, the examples, it's all one big blob.
And Claude sees it that way too. Right. Until
you use these markers. Tags like instructions
or document act like highlighters for the AI.
It helps it cleanly separate the different parts
of your prompt. So you're creating structural
blocks for it. Exactly. You wrap your source
material in a document tag, your instructions
in an instructions tag. This has a huge effect
on how the model parses everything. It gets much
cleaner, much more predictable. Whoa. So you
could use tags to manage, like... Dozens of different
documents in a single prompt for a huge comparative
analysis. Now you're getting it. It's like stacking
Lego blocks of data. It's a game changer for
managing these massive context windows. That
makes so much sense. And then rule eight is for
any kind of sequential work. If you're using
an agent workflow, you have to announce it. You
mean for tasks that take multiple steps? Yeah,
any back and forth. You have to tell the AI that
the job is ongoing. Otherwise, its context could
just reset between turns. You're basically giving
it short -term memory. So just a single sentence,
like you are an agent in a multi -turn workflow.
That's it. That one line forces it to track its
own progress and saves you from repeating yourself
over and over. Okay. Rule nine is about external
tools, right? Like a calculator. Yes. And the
guidance here is don't scream must. If you command
it, you must use the calculator for all math.
You get clumsy results. It'll call the calculator
for 2 plus 2. Which just slows everything down.
You want it to use judgment. Precisely. So you
use softer guiding language. You should use the
tool when you need to perform complex calculations.
This lets Claude exercise some judgment and only
use the tool when it's actually helpful. It's
a much smarter outcome. Much smarter. And finally,
rule 10 is one of the more fascinating ones.
If your output feels sloppy. Avoid the word think.
Really? Avoid think? It sounds like a superstition,
I know, but the testing on this is consistent.
Asking the model to think seems to invite speculation,
maybe shallower reasoning. So what do you use
instead? Stronger, action -oriented words. Consider,
analyze, evaluate. You're demanding a more structured
step -by -step process. It's like analyze forces
it to show its work, whereas think lets it just
reassociate. That's a perfect way to describe
it. It shows that good prompting is as much about
empirical observation as it is about perfect
logic. Beyond the prompt itself, what changes
when the model expects an agent workflow? It
starts tracking progress and preparing for the
next step. It avoids looping or restarting from
scratch. When you tie all of these rules together,
the big idea that emerges is that the AI is,
at its core, intensely literal. And it really
wants to be helpful. You're moving away from
treating it like a search engine. Exactly. You
stop whispering at it and you start treating
it like a highly specialized, hyper -competent
employee. So the core transformation for you,
the listener, is to really embrace being the
director. Claude is the actor and it's just waiting
for precise instructions. And when you give it
that specificity, that intent, and that clear
structure. the results shift almost immediately.
From generic to really professional grade output.
So here's our call to action. We encourage you
to open Clog right now or whatever model you're
using and just apply rule number one, hyper -specificity,
and rule number two, the intent principle. You're
going to see a difference right away. An immediate
difference in the quality of what you get back.
And maybe here's a final thought to mull over.
If we need this level of precision, this much
intent and structural clarity to communicate
well with an AI, what does that tell us about
the clarity we owe to the other complex humans
in our lives? Something to think about. Yeah.
As you write your next prompt. Thank you for
sharing your source material for this deep dive
into the art of AI orchestration. We'll catch
you next time.