The difference between amazing AI output and
that frustratingly generic robotic nonsense isn't
actually the model you're using. You could be
on the most advanced LLM available. The key is
simply how you ask the question. We have to start
treating our AI like a digital magic eight ball
that just gives these quick, shallow answers.
Welcome to the Deep Dive. Today we're unpacking
a truly comprehensive guide. It details Anthropic's
10 advanced rules for prompting Claude Opus 4
.5. And this deep dive is customized for you,
focusing on the mechanics of getting higher quality,
more reliable work. It's a fascinating look under
the hood. I think most of us are only tapping,
what, maybe two -thirds of the model's brain
power because our prompts are just lazy. We're
asking for coffee when we should be handing the
AI a detailed multi -step recipe. Exactly. The
mission here is simple. We're moving past these
vague, single -line requests and learning how
to turn a large language model into a strategic
thinking partner. This is all about applying
clear context, specific constraints, and structured
prompting to really 10x the quality of the insights
you get back. So we've broken this guide into
four core themes. First, we'll look at adopting
a genuinely collaborative mindset. Then, we'll
cover how to control the creative boundaries
of the AI. After that, we'll delve into why breaking
massive tasks down always works best. And finally,
we'll analyze the secret power phrases that actually
unlock Claude's deeper, more complex reasoning
abilities. OK, let's start with that mindset
shift. The first two rules, they deal directly
with how you approach the AI. And frankly, this
is where most users fail. They're either overly
polite or, you know, on the other hand. too bossy,
demanding things instantly. Neither tone works
well with these advanced models. Right. We often
forget that these are trained on human conversation,
which means tone matters, but maybe not in the
way we think. Rule one says we have to treat
the AI like a high -performing teammate, not
a servant. That's the distinction. The core requirement
is always clarity and directness, but it's got
to be delivered in a friendly, cooperative tone.
If you barked an order at a new team member,
you wouldn't get their best thinking. But at
the same time, treating the AI as if it requires
constant thank yous or flowery language just
adds unnecessary tokens and noise to the prompt.
So if we're correcting a document, the shift
is away from just saying, correct this. Instead,
we frame it as a goal, something like, please
review this draft for grammatical errors and
suggest three distinct ways I can modify the
language to sound more professional and confident
for a board meeting. That gives it the context
and the tone. And that leads perfectly into rule
two, which is the principle of explicitness.
If collaboration is the attitude, then details
are truly your superpower. If you don't explicitly
specify what you want, you basically guarantee
you'll get the bland, generic answers. The fluff.
That unusable content that everyone complains
about. Exactly. That's the key takeaway right
there. Our source material emphasizes that you
have to specify three crucial things. Quantity,
topic, and audience. Don't just ask for some
blog post ideas. That gives the AI infinite possibilities,
so it just chooses the safest, most generic path.
But if you specify, generate 10 compelling blog
post titles about remote work's unexpected impact
on urban planning, targeted specifically at city
officials and real estate developers, well, suddenly
the scope is narrow. The goal is clear and the
output quality just skyrockets. And here's where
the analysis gets deep. Using strong action verbs
like generate, analyze, compare, critique, that
isn't just a stylistic choice. It actually forces
the model into a specific operational mode. Correct.
It tells the model precisely what cognitive function
to perform. Asking the AI to analyze a financial
document forces it to use different, more detailed
reasoning pathways than if you just asked it
to write about the document. It moves the AI
from summary mode. to an active operational mode.
So if clarity is everything, how critical is
it to define the specific action we want it to
take? Action verbs move the AI from summary mode
to operational mode. Let's move to how we control
that output itself. Because getting the model
to think hard is one thing. Getting it to produce
something usable is another. Rule three is all
about defining the boundaries. This is the paradox,
right? That creativity actually thrives on constraints.
When you give the AI an open -ended request,
it just gets lost in all the possibilities. No
structure equals chaos and, honestly, predictability.
I love the analogy from the source material.
It's like putting a fence around a garden. Without
limits, the output just goes wild, and it often
defaults to the most common words. Constraints
force it to search deeper in its knowledge space.
Yeah, think about this prompt. Write a 500 -word
short story about a robot detective on Mars in
the style of Raymond Chandler, but do not use
the word cyber. red planet, or metallic. That
is a deliberate constraint. That's fascinating.
So limiting the AI's options, especially by banning
common words, forces it to think harder and be
more specific. It has to make these non -generic
creative decisions because you've kind of tied
one hand behind its back. It can't lean on those
high probability phrases. And speaking of structure,
rule five demands structured output. We need
to actively stop accepting those long, undifferentiated
walls of text that are hard to scan or integrate
into other workflows. This is where we shift
the responsibility for presentation onto the
AI. Claude defaults to paragraphs because that's
the most common text structure. But you can and
you must request precise formats. We're talking
about markdown tables, JSON or CSV. Right. And
for listeners who might not know, JSON or JavaScript
Object Notation is just a clean, standardized
format that machines and programmers use to exchange
structured data. Requesting data this way is
incredibly efficient. The practical application
is huge. So instead of asking for information
about the last three Apollo missions, 15, 16,
and 17 in PROS, you request a structured table,
include their launch dates, the crew, and a key
scientific achievement. And what you get back
is immediately functional, sortable data. But
why does requesting a format like JSON or a table
instantly elevate the quality of the output?
Structured formats eliminate guessing and make
the output immediately usable. That makes perfect
sense. Now, this leads us nicely to the iterative
workflow. Our sources are insistent that trying
to get a perfect final draft in one shot is a
fundamental trap. It's unrealistic and always
frustrating. So rule four demands we start with
an exploratory draft. Yeah, we're moving past
that all or nothing approach. The process has
to be broken down. Always ask for an outline
or a plan first. This lets you course correct
early before the model burns a ton of time and
tokens on a 5 ,000 word draft that missed the
mark on section two. I still wrestle with prompt
drift myself. Beat. For those unfamiliar, that's
when the AI slowly forgets your original constraints
or the core topic as the conversation gets longer.
It's incredibly painful when you realize late
in the process that a major assumption was wrong
and you have to just throw everything away. Getting
that outline right prevents that catastrophe.
And if you apply that microstep thinking on a
larger scale, you get rule 10, divide and conquer.
Complex tasks overwhelm both the user and the
model. you have to act like a conductor. breaking
the entire symphony into controllable, smaller
sequential stages. Let's use the business plan
example. You never ask for the whole thing at
once. You first request the table of contents,
then you prompt for the executive summary, then
the market analysis, and only then do you ask
the AI to synthesize and check for contradictions
between the sections. The efficiency boost is
just dramatic. You maintain quality control at
every single small step. You're reviewing the
integrity of the brick, you know, before you
let it become part of the wall. It prevents costly
rework later. When dealing with huge documents,
what is the single biggest benefit of the divide
and conquer approach? Breaking tasks into chunks
ensures high quality control at every stage.
Okay, let's pivot now to what I think is the
most exciting section. Rules six through nine.
This is the toolkit for unlocking the model's
deepest reasoning capabilities. Rule six is simple,
but it's transformative. Explain the why. Context
is king. The AI needs to understand the underlying
purpose, the ultimate goal behind your request
to deliver truly tailored results. It's the difference
between a tool and a partner. Right. If you just
ask for five coffee slogans, you get the most
common answers, great coffee. But if you explain
that the beans are ethically sourced, the target
audience is environmentally conscious millennials,
and the medium is a specific Instagram ad, well,
then everything changes. That context shifts
the entire output from generic marketing to tailored
high value branding. It narrows the vocabulary
and the concepts the AI even considers. And rule
seven is about controlling brevity versus verbosity.
Claude has no idea if you want a detailed dissertation
or a bullet point summary unless you tell it.
So you have to explicitly set the level of detail
up front. We see this when comparing reasoning
levels. Take a single topic, like photosynthesis.
You can request deep thinking by stating, explain
photosynthesis in detail for a university -level
biology student, making sure to include complex
concepts like C3 and C4 pathways. Or you simplify
drastically. Explain photosynthesis like I'm
five years old, using only analogies about baking
and sunlight. The third level of control, rule
eight, is providing a scaffold. This is where
templates become your secret weapon because they
enforce consistency. Stop asking Claude to summarize
an article generally. That just leads to inconsistent
links and formats. Instead, you give Claude a
precise format, a summary template that explicitly
states, main thesis, one sentence, key supporting
points, three bullet points, and a concluding
insight, one sentence. The AI has to fill in
the blanks consistently every time, making its
output easily ingestible. And finally, rule nine,
use power phrases and expert personas. These
are the immediate triggers that unlock Claude's
higher level reasoning and access a much more
sophisticated vocabulary. The power phrase toolkit
is essential for advanced users. You use think
step by step to explicitly force reasoning. It
makes the AI lay out its internal logic before
giving the final answer. You use critique your
own response to trigger self -correction and
iterative quality assurance. And the persona
rule. Adopt the persona of an expert in field.
It primes its domain knowledge. You're telling
the model which part of its massive data set
to privilege. Act as a leading constitutional
lawyer is just so much better than tell me about
the First Amendment. Whoa. Imagine scaling this
iterative self -critiquing process where the
model checks its own work across a million queries.
That is how we unlock true, reliable expertise
from these systems. You are leveraging the model's
ability to self -audit. Which of the power phrases
is most effective at forcing the AI to slow down
its processing? Think step -by -step forces the
model to show its actual reasoning. So to summarize
the big idea for you, these rules are simple,
but they're powerful. Be explicit in your brief,
set deliberate creative boundaries, iterate in
small stages, and use specific linguistic triggers.
These aren't just minor tips. They are battle
-tested techniques designed to eliminate generic
output. Let's run one quick synthesis example
to show how these rules stack up. Compare the
vague request, tell me about stoicism, which
gives you a generic introductory paragraph versus
the correct way to prompt. The right prompt stacks
six of these rules together. First, we use the
persona rule, act as a university professor of
ancient philosophy. Then we explain the why,
the context. I am preparing a one -hour introductory
lecture for first -year students with no prior
knowledge. Next, we use divide and conquer. First,
create a lecture outline with three main balanced
sections. We constrain the scope. The outline
must have a clear introduction, a body focused
on practical application and a strong conclusion.
Then we demand structured output. Please format
this as a nested bulleted list using markdown
headings. And finally, we use explicitness. For
each major body point, include a key stoic figure
such as Seneca and one core actionable idea.
And the result is immediate. It's structured,
it's audience -appropriate content, a professional
lecture outline ready to teach from. The difference
between that unusable AI slop and high -quality
insight is solely in the 30 seconds you took
to craft that powerful prompt. Claude is an extremely
sharp tool, but it's only as effective as the
person wielding it. You need to take those few
extra seconds to set your brief. Add context,
set boundaries, and deploy those power phrases.
That's what turns a chat experience into a strategic
partnership. So think about one complex information
-gathering task you usually rush through in one
prompt. How would breaking it into three smaller,
constrained, and persona -driven prompts change
the outcome? That's your next deep dive. Thank
you for joining us for this deep dive.