You ask your AI a simple question today. Right.
Expecting a really quick, helpful answer back.
But instead, you get a rambling 300 -word essay.
It's an incredibly frustrating experience for
all of us. Beat. Today, we're going to fix this
wordiness forever. You're going to keep your
chat interfaces completely clean. Welcome back
to another deep dive. You are the learner, and
we're glad you're here. We're unpacking a powerful
three -layer system today. It guarantees concise,
razor -sharp AI responses every single time.
We're going to cover the prep, the prompt, and
the rework. This framework is going to save you
a massive amount of time. Beat. I have to make
a vulnerable admission right up front. Oh, yeah.
What's going on with your prompts? Well, I still
wrestle with massive walls of AI fluff myself.
It happens to the absolute best of us, honestly.
I'll ask for a basic coding fix while working.
Let me guess, you get a whole history lesson.
Exactly, it gives me a massive textbook on programming
history. It really makes you wonder why the default
is so verbose. What is happening under the hood
that makes it over explain? It all goes back
to how these massive models actually learn. Beat.
Let's start right at the root then. Why is the
AI so incredibly wordy anyway? What is the underlying
mechanism causing this behavior? Well, there
are kind of two big training phases here. First,
the AI basically reads the entire internet for
data. So it's consuming millions of books and
long forum posts. Exactly. And most human writers
naturally tend to over -explain things. Bloggers
repeat concepts to help readers understand them
much better. So the AI simply copies that repetitive
human writing habit. Yeah. And then comes the
second major training phase. Read. That's what
the tech industry calls RLHF, right? RLHF means
real people rating answers to teach the AI. Right.
And let's think about the psychology of those
readers. If you get paid to evaluate an AI's
factual answer. You naturally want to reward
toward extreme thoroughness and total caution.
Exactly. A massive comprehensive essay feels
much safer to upvote. It feels way safer than
a single blunt factual sentence. Over millions
of interactions, the AI learns a very clear lesson.
It learns that being incredibly verbose usually
gets a good grade. So telling it to just be concise
usually fails entirely. The model is fundamentally
wired to think more is better. Yeah, it genuinely
thinks it's giving you the best service. Beat.
Let's think about the coffee example I tried
recently. Oh, I love this one. What did you ask
it? I asked for simple pour -over coffee steps
for my morning. And it gave you the entire history
of coffee, right? I got a massive paragraph on
Ethiopian highland coffee beans. I got another
whole section on the ideal water pH level. Then
I got health warnings about daily caffeine intake
limits. The AI genuinely felt it was being incredibly
helpful there. A human raider would have probably
loved that thorough answer. But for a busy user
making breakfast, it's completely useless. This
creates a massive hidden cost in the overall
system. Long answers do not just waste your valuable
personal time. They actually degrade the AI performance
over a long chat. Pete, this brings us to the
context window limit. Context window is the AI's
short -term memory capacity. Right. And for ChatGPT,
it handles about 128 ,000 tokens perfectly. Tokens
are just chunks of words, the AI processes. That
equals roughly 100 ,000 words in total. It sounds
like a totally endless amount of space, but it's
exactly like stacking Lego blocks of data. Every
single extra word is another Lego block used
up. Think of it exactly like your phone's internal
storage space. If it gets totally full of junk,
the system drags. Long rambling answers quickly
clog up the AI memory links. It makes the AI
forget earlier details in your conversation.
I spent months testing this with various travel
planning prompts. What happened when the context
windows started getting full? In one long chat,
it started mixing up European cities. That happened
by the 10th reply quite easily. The short -term
memory window was totally clogged up. Completely
clogged up. And research from Anthropic actually
backs this concept up completely. They found
that compact responses maintain high quality
much longer. The model stays significantly sharper
when it speaks a lot less. Beat. Do long answers
actively make the AI dumber over a long chat?
They absolutely do. The useless fluff physically
pushes out the vital core facts. The AI literally
runs out of working cognitive space. It has to
drop older context to fit new fluff. So strict
limits actually keep its memory sharp. Exactly.
Two -sec silence. Let's move into layer one.
This is what we call the critical prep phase.
This involves using specific permanent system
instructions. Most major AI platforms have a
spot for system instructions. You can set them
in custom GPTs very easily today. In Google Gemini,
you will find it under their gems. And for Claude,
you use their specific projects feature. These
instructions act like a permanent invisible background
guide. Why does this work so incredibly well
for users? Because the AI always reads these
rules first. It processes them deeply before
it ever sees your prompt. It fundamentally shapes
how the entire neural model thinks. Adding rules
here cut my response lengths immediately. They
literally dropped by half right away. We need
to define those exact length limits clearly.
You recommend setting specific rules for different
question types? You can literally copy these
rules right into your settings. For simple questions,
demand exactly one to two sentences always. For
medium questions, use a strict three to five
sentence limit. Complex questions get a single
paragraph summary at most. Then, you allow it
up to five concise bullet points. Beat. There
is one crucial addition to these rules. Start
right with the answer. Stop after the last key
fact. Fixing a flat bike tire is a great practical
example. You're stuck on the side of the road
with grease. You do not want the long history
of vulcanized rubber. You just need to know how
to use the levers. Right. And the system instructions
fix that scenario perfectly. Because you defined
a complex question structure long beforehand.
The AI knows to give one quick summary paragraph.
Then it drops straight into five actionable,
clear bullet points. You fix the flat tire and
get riding again. Beat. You also emphasize focusing
heavily on positive directions. Yes. Always tell
the AI exactly what it should do. Never tell
it what it needs to actively avoid doing. Say
something like, lead with the key info. Exactly.
Do not say, no long intros. This relies entirely
on how AI processes human language natively.
Negative commands often trigger the exact opposite
behavior. It is a truly fascinating quirk of
the system. It's exactly like telling someone
not to picture a red car. You immediately picture
a bright red car in your head. Positive rules
give cleaner results 80 % of the time. Once this
prep layer is set, it runs perfectly. It essentially
runs on autopilot for every single chat. Beat?
Why do negative words confuse its processing
so much? It really comes down to predictive text
math. When it reads the word intro, it activates
those pathways. It naturally wants to write an
intro next. It struggles to calculate the mathematical
concept of not. Got it. Negative words just confuse
its prediction math. That is exactly it. Two
secs silence. Let's slide into layer two now.
This is what we call the dynamic prompt phase.
We're basically using formatting as a rigid cage.
I absolutely love the tiger in a cage analogy
here. Giving an AI a completely blank page is
highly dangerous. It will inevitably write so
many useless, fluffy things. But when you give
it a specific visual frame... Right, it has to
stay inside that tight box. The visual shape
actually matters much more than your words. It
all comes down to the underlying next word, logic.
the AI always tries to guess the very next word.
Usually it starts with something like, yes, I
can help. That polite opening is exactly where
the long talking starts. But if you force it
to start differently entirely, like forcing it
to start with a table line character, or forcing
it to use a strict number one, it has absolutely
no chance to say hello first. It knows it is
inside a very special box immediately. AI follows
a visual format much better than vague adjectives.
The word short, is really hard for it to understand.
But column A is a very specific, rigid spatial
place. Beat. Putting AI in a table creates strict
visual boundaries. It turns on a special save
words mode internally. The AI mathematically
knows table cells are very small spaces. It automatically
removes filler words like furthermore or additionally.
It focuses totally on raw, real facts instead.
It drops the pretty essay writing completely.
It's beat. Let's build a comparison table prompt
together now. Say you run a small local retail
shop. You want to compare email marketing versus
social media ads. A typical bad prompt gives
a massive rambling essay back. But a really good
prompt asks for a strict table. You ask for clear
rows covering cost and overall reach. You add
setup time and the expected final business results.
The contrast in what you get is incredibly stark.
Under email cost, it just prints the word low.
Under social ads cost, it simply prints higher.
It gives you the raw facts almost instantly.
There is absolutely no fluffy intro or tedious
wrap up text. If you do not like using data tables
daily, you can also use a very simple numbered
list format. But you absolutely must add a strict
word cap constraint. Without a limit, it gives
20 incredibly long, boring points. I really love
using the max 15 words rule. It is a truly brilliant
trick for strict word budgets. Ask for five reasons
your digital ads have no buyers. Add that each
reason must be maximum 15 words exactly. The
AI must think extremely hard about its choices
now. It has to carefully choose the absolute
best words available. It runs out of word budget
instantly otherwise. You can also use strict,
step -by -step sequential limits. Tell it exactly
how to run a Facebook ad campaign. Write five
steps, exactly one simple sentence each. Instead
of a scary guide, you get simple, actionable
steps. Indeed. Does a visual table physically
change how it searches for facts? It really changes
the entire internal generation process completely.
It stops pulling fluffy context to fill small
data cells. It restricts its search strictly
to raw data points. Visual boundaries force it
into a strict word budget. Exactly. It works
perfectly every single time. Two sec silence.
Welcome back to the deep dive. Even with great
prep, some long answers just slip through. That
brings us nicely to layer 3 today. This is what
we call the dynamic rework phase. It's basically
like editing heavily after the first draft. You
really need to use the quick sharpen trick here.
If you get a really wordy reply back, you reply
with a very specific, rigid command string. Cut
this by exactly 60%. Take out all the vague parts.
Use only direct, active sentences. Beat why specify
exactly 60 % for the cut. It seems mathematically
specific for the AI to handle. Yes. The AI calculates
that exact percentage mathematically very well.
Take out vague parts. Specifically targets those
fluffy, weak statements. It naturally drops evasive
phrases like Well, it depends. Direct sentences
keep the final output incredibly snappy. 200
words on exercise routines shrinks incredibly
fast. It suddenly becomes 80 words of pure, actionable
steps. It sharpens everything without losing
any core, vital info. They scour web data and
build massive, comprehensive reports. Sometimes
these AI reports are 30 pages long. Dealing with
those massive files requires a special trick.
You copy the entire massive text report first.
Then you open a brand new, blank chat window.
You paste it and give a very strict prompt. Pull
out only the main actionable steps from this.
Skip all the background history and the extras.
Focus strictly on how to apply it today. For
a dense business plan report, this is pure magic.
It turns 5 ,000 words into 500 easily. Beat.
Whoa. Imagine it compressing a 5 ,000 -word monster
report into 500 actionable words in seconds.
It really feels like having a secret research
superpower, but you should ask yourself if you
actually need deep research. For most daily tasks,
you absolutely do not need it. Just use a strong
model like GPT -5 .2. Use it with the stand -old
web search feature turned on. Basic search covers
85 % of needs much quicker. Save the deep mode
strictly for major market analysis projects.
Beat. We also definitely need to discuss chain
prompts. I used to write massive three page prompts
myself. The AI often ignores vital rules hidden
in the middle. Researchers actually call this
the lost in the middle problem. AI usually remembers
the start and the very end well. But it completely
forgets the center of long prompts. You need
to break complex prompts into small, logical
steps. It totally removes that forgotten middle
part entirely. The feedback loop and chain prompting
is truly amazing. You execute step one. and you
carefully review it. If the tone is slightly
wrong, you fix it immediately. You correct it
before ever moving to step two. If you wait until
step 10, the output is ruined. You have to rewrite
the entire three pages again. Small steps keep
the AI completely focused and grounded. It creates
a powerful real -time interactive feedback loop.
It is exactly like a teacher and student working
together. You catch and fix tiny mistakes as
you go along. Why is opening a brand new chat
for the report summary so vital? It completely
clears the context window of previous conversational
baggage. It stops old wordy fluff from bleeding
into the new summary. A fresh chat wipes the
memory slate completely clean. Spot on. Two sec
silence. Let us slowly synthesize this entire
system. We have built a powerful three -layer
system today. Layer 1 is the crucial prep phase.
You cement positive system instructions and hard
sentence limits. You set these up before you
ever start chatting. Layer 2 is the rigid prompt
phase. You build visual cages using data tables
and lists. You strictly apply word budgets to
every single request. Layer 3 is the dynamic
rework phase. You use the exact 60 % cut rule
often. You use chain prompting to edit everything
on the fly. The ultimate payoff here is really
quite massive. Getting straight to the point
saves your valuable personal time. But it also
perfectly preserves the AI short -term memory.
It keeps the cognitive tool razor sharp for complex
tasks. Your chat environment stays incredibly
clean and highly useful. Beat, I want you to
start very small today. Do not try to overhaul
everything at once. Go into your AI background
settings later today. Just add one simple positive
rule about output length. See how it completely
transforms your daily conversational results.
You really must become the strict boss of it.
Beat. I want to leave you with a final thought.
We constantly force these incredibly vast models
into tiny boxes. We demand strict, concise, rigid
outputs from them constantly. If we constantly
do that, do we ever risk losing the beautiful
unexpected connections they might make? What
if we just let them wander a little bit? Beat.
Thank you for taking this deep dive.