You know, a lot of us have had this exact feeling.
You open up a powerful AI tool, something like
ChatGPT or Claude, you type in a question, maybe
something complex, something important, and the
answer that comes back is just, well, it's flat.
Generic. Yeah, it's generic. It doesn't sound
like your company It definitely doesn't sound
like you hear this all the time and people blame
the tech They think the models themselves just
don't have real insight But the core discovery
from the sources you shared about the art of
asking is it's something totally different Okay,
let's unpack that because the sources are suggesting
the problem isn't the AI's intelligence at all
It's our interaction style. It's that we're talking
to this incredibly powerful tool like it's a
search bar Exactly. Welcome to the deep dive.
If you treat AI like Google, you're going to
get generalized Google results. We have to start
treating it like a specialized teammate. I mean,
think of it like this. It's an eager, brilliant,
super fast intern who is just desperate to please
you. But. And this is the key part, an intern
who absolutely hates saying, I don't know. That
eager intern analogy feels so right. So our mission
today is to distill this massive shift in approach.
We're moving from treating AI like an oracle
to treating it as this highly capable but kind
of naive collaborator. And we're going to dive
into five simple, really powerful techniques
that experts use to get intelligent, personalized,
and most importantly, reliably accurate results.
This is all about something we call context engineering.
Let's start right there. What is the fundamental
problem we're trying to solve with this idea
of context engineering? It's that eager interim
problem you just mentioned. The AI is fundamentally
programmed for helpfulness. So in its training,
providing an answer, any answer, is prioritized
way above accuracy or even honesty. So if you
give it vague instructions, it has to guess the
context because it just can't tolerate silence.
It has to respond. And that guessing game, that's
what leads to the biggest headache in using AI
today. That's right. If the AI doesn't have the
context that's in your head, it just makes things
up. I mean, that's the literal definition of
hallucinations. The AI is generating fiction
because it's trying so hard to please you with
a response. It just, it fills in the gaps. So
if prompt engineering sounds a little too technical
for people, we should think of context engineering
as just bridging that gap. Bridging the gap between
what's in our brain and what the AI needs to
know to actually succeed. Precisely. If I walk
up to you and just say, write a budget, you have
to know who it's for, what the goal is, the time
frame. Context engineering is just making sure
you transfer all those constraints to the AI
before it starts writing. So if that eager intern
is so prone to guessing when we're vague, What's
the biggest risk of providing insufficient context?
Guesswork directly leads to hallucinations, because
the AI will simply invent details to satisfy
the request. That makes perfect sense. Okay,
let's get into the first technique, which seems
to directly address the speed and, frankly, the
recklessness of this intern. Chain of thought
reasoning. This is so critical. When an AI generates
a response, it's not planning a full essay first.
It's predicting one single word at a time based
on the words that came before it. It's like speaking
without really thinking deeply. Right. It's like
a student just shouting out the first answer
that pops into their head for a hard math problem.
And that's often the wrong one. We need to force
it to be more deliberate. We have to force the
AI to show its work. And you don't need PODE
for this. You just need to add one magic sentence
to your instructions. Before you answer, please
walk me through your thought process step by
step. And I find this fascinating because the
sources noted that making the AI write down its
plan actually makes the final output smarter.
It literally uses those planning words to predict
better words that come after. It's like it's
building a higher quality scaffold for its own
answer. It really does. Let's use a real -life
example. Planning a budget trip to Japan. A bad
prompt is just, plan a five -day trip to Tokyo.
Make it cheap. The AI will just spit out a generic
itinerary. You'll get expensive hotels, some
tourist traps. It's useless. But the good prompt,
the chain of thought prompt, specifies the details
and then demands that planning phase. Something
like... I need a five -day itinerary for Tokyo
on a tight budget. Before you create the itinerary,
please think through the constraints step -by
-step. Prioritize low -cost transport, research
cheap but high -quality food, and only suggest
free activities. Then the AI starts by saying,
okay, my first constraint is the budget, so I'll
prioritize subway passes and skip the high -speed
rail. For meals, I'll look at convenience stores.
That thought process locks in the constraints,
so the final itinerary is something you can actually
use. And here's a bit of a vulnerable admission.
I still wrestle with prompt drift myself. You
know, when the final itinerary looks good, it's
just so easy to skip reading that initial thought
process. Yeah. But we have to read the thinking
part, too, right? Oh, absolutely. Because if
the AI makes a bad assumption in its planning,
like, say, it assumes you're flying into an airport
that you're not, you have to catch that flawed
premise. Right. If you only look at the final
answer, you've completely missed the core problem.
It allows us to course correct before the AI
commits fully to a bad direction. Yes. It addresses
that fundamental flaw of speed over precision.
Okay, so if chain of thought helps us improve
the AI's logic, the next big challenge is making
the AI sound like a person. Specifically, like
you. This brings us to technique two, few -shot
prompting. And this one tackles that really common
mistake of using weak adjectives. I see people
type, write a professional, engaging, funny email.
Those words are subjective. They're basically
meaningless to an AI that's trying to match a
specific human voice. Adjectives are weak. Examples
are strong. The AI is a phenomenal copycat machine.
It's just brilliant at pattern matching. If you
want it to write like you, you have to give it
the data, the patterns of what you sound like.
And the technique is surprisingly simple. You
just find three to five examples of your own
successful writing, emails that got a great reply,
short reports you were proud of, and you paste
them right into the chat. And the instruction
is key. You ask the AI to Analyze these examples,
then write a new email using the same style,
tone, and format. It's like it's using your own
work as a microtraining set. Let's use that example
of writing a statistical topology letter. A bad
prompt is just, write an apology email. Be polite.
The result. Dear valued customer, we are deeply
sorry for the inconvenience. It's so robotic.
It just alienates people. But the good prompts
gives it short, friendly examples. Something
like, hey, Sarah, I saw your order was late.
I am so sorry about that. And the AI picks up
on the short sentences, the casual greeting,
and it avoids all that corporate jargon. The
apology it writes is personal and actually effective.
OK, but let me ask a practical question here.
If I paced in three or five paragraphs of my
best writing, doesn't that make the prompt kind
of massive? I mean, does that get slow or expensive
to run every single time? That's a great point,
especially if you're hitting your context window
limit. But remember, the examples don't have
to be long. They just need to be representative.
A few strong short paragraphs are usually enough
for the AI to pick up on your linguistic fingerprint,
your sentence structure, your vocabulary, your
rhythm. The value of getting a perfect output
really outweighs that slight increase in the
input data. And what about the opposite? Can
you show it what you don't want? Absolutely.
That's the advanced tip here. Using a negative
example, you paste in a piece of writing you
hate, maybe a super stiff formal corporate email,
and you explicitly tell the AI, do not write
like this, avoid this style. It learns just as
quickly what to avoid. So how many examples are
generally effective for the AI to pick up on
a specific style or voice? We're aiming for three
to five strong examples of your best work. That's
a tangible number. Oh. OK, moving to technique
three. Reverse prompting. This feels vital for
those times when, as the user, you don't even
know what information is important to share.
This happens all the time with health and safety.
Remember the eager intern. If you ask for a workout
plan but you forget to mention an old me injury
or that you only have dumbbells at home, the
AI just guesses. And it might give you this generic,
intense plan that could actually hurt you because
it's missing that vital context. So we flip the
script. Instead of us giving vague instructions,
we force the AI to become the smart interviewer.
The key sentence is so simple. Before you generate
the response, please ask me any questions you
need to know to do the best job possible. This
forces it out of guessing mode and into information
gathering mode. We can use that gardening scenario
from the sources. A bad prompt is just, tell
me what to plant in my garden. The AI guesses
you're in a temperate zone and recommends tomatoes,
but you live in a cold climate where they'll
die instantly. Right, but the good prompt makes
the AI ask five clarifying questions about your
location, hours of sunlight, your maintenance
level, whether you want edible plants. You answer
those five questions and the plan it gives you
is perfectly tailored and will actually succeed.
So it seems like it saves a lot of time. You
just bypass several rounds of bad answers. It
absolutely does. It moves straight to an informed
answer, which increases the speed of execution
by eliminating all those useless revisions. Technique
4 is incredibly powerful. And it goes back to
the AI's massive training data. Let's use the
library analogy. If AI is this huge digital library
with every book in the world, from comic books
to PhD textbooks, a generic question gives you
the average of everything. And we don't want
the average answer. We want the expert answer.
So when you assign a role, you're telling the
AI which section of the library to look in. You're
telling it to ignore the blogs and focus only
on the specialized high authority texts for that
role. The structure is simple. but really effective.
You are an expert job title with 20 years of
experience. You think like a famous person. This
immediately filters the knowledge the AI uses.
Let's say you're checking a resume. A bad prompt
is just, check my resume for mistakes. You'll
get basic spell check, maybe some grammar notes.
That's the average answer. But the good role
assigned prompt is, you are a strict hiring manager
at a top tech company. You reject 99 % of resumes.
Tell me why you would reject mine. The AI completely
changes its persona. It will ignore the typos
and tell you your bullet points are too vague,
that you lack quantifiable achievements, that
your structure isn't optimized for automated
tracking systems. That kind of high -stakes feedback
is invaluable. And you can assign almost any
persona. a tough negotiation expert, certified
financial planner, even a friendly kindergarten
teacher to simplify a really complicated topic.
Oh, and just imagine the potential there. When
you can instantly tap into the synthesized knowledge
of a billion textbooks just by assigning a job
title, you're accessing true, focused expertise
instantly. You're bypassing all the noise. So
what is the critical difference between the generic
answer and the role assigned answer? Role Assignment
accesses specific, high -quality knowledge instead
of providing an average of all possible information.
Okay, finally we get to Technique 5, Role Playing.
And this feels like the ultimate application
of Persona, turning the AI into a conversation
simulator. Right. Pilots use flight simulators
to crash planes safely until they know exactly
how to handle turbulence. We can use AI as a
conversation simulator to practice high -stakes
talks, asking for a raise, delivering bad news,
so you don't mess up the real interaction. The
sources outline a really powerful three -window
system for this. Let's use that example of the
tough rent negotiation with our fictional landlord,
Mr. Smith. Okay, so step A happens in chat one,
the profiler. You build the character, you say,
Mr. Smith is stubborn, he talks loudly, he's
60 years old, and he always complains about property
taxes. You use the AI to really flesh out that
psychological profile. And then step B is chat
two, the simulator. You paste that detailed description
in, you set the scene, and you start the conversation.
You are Mr. Smith. Stay in character. Ring ring.
And when you make your offer, the AI, acting
as Mr. Smith, will argue back. It'll challenge
your assumptions, it'll maintain that stubborn
persona. This is where you get to crash safely.
You can try five different opening lines. You
learn what makes him angry, what diffuses him,
what objections he's going to raise. And then
step C is the key. You integrate a technique
we mentioned earlier. It's chat three, the coach
and the Russian judge. You copy the entire transcript
of your negotiation attempt and you paste it
into a new chat window. Right. And now you assign
a new specific role. You are a world -class negotiation
coach. You must grade my performance, but you're
a Russian judge. do not be polite, grade me on
a scale of 1 to 10, and be brutal. Why is that
separate coach step necessary rather than just
asking the simulator for feedback? It ensures
the analysis is objective, separating the character
simulation from the performance review. Got it.
And because the AI is explicitly told not to
be nice, it will point out the logical holes,
the emotional traps you fell into. That truth
is way more helpful than just simple encouragement.
Exactly. We usually only get one shot at a hard
conversation. With this method, you can... Practice
10 times until you're calm, you're prepared,
and you're ready for the real call. Okay, to
wrap this deep dive up, let's hit one final technical
tip. This is the new chat rule. It's so simple,
but it's crucial. Keep your workspace clean.
The AI relies on the previous conversation history
for context. If you change the topic, just start
a fresh chat window immediately. Yeah, we've
all done it. We ruin a perfectly built persona
by asking an unrelated question. You don't want
to break your serious CEO negotiations simulator
by asking it about your fantasy football picks
in the same thread. A long, messy history just
confuses the AI. It's like using one notebook
for math, history, and cooking. One focused task,
one clean chat. That makes sense. OK, let's quickly
summarize these five techniques we covered. One,
chain of thought. Force the AI to think step
by step for smarter, more logical outputs. Two,
few -shot prompting. Provide three to five examples
of your work for perfect style and tone matching.
Three, Reverse prompting. Ask the AI to interview
you to get that critical, perfect context. Four,
assign a role. Make the AI an expert to access
high quality, focused advice. And five, role
playing. Use that three window system and the
Russian judge to simulate and practice high stakes
conversations safely. The core idea really is
the same across all of these. AI is the best
teammate you've ever had, but it only shines
when you give it precise instructions. So stop
talking to it like an omniscient database. Start
talking to it like the smart, eager intern it
is. An intern who needs clear constraints and
boundaries. And our challenge to you is simple.
Don't try to master all five techniques today.
Just pick one. Maybe use FewShop for an important
email you have to write this afternoon. Or try
Chain of Thought for a complex decision you're
trying to map out. Just try one right now. And
as a final provocative thought for you to explore
this week, if we have to explicitly tell the
AI to be brutal and honest, how does that fundamental
AI tendency, the need to be helpful over being
truthful, reflect broader human behavioral tendencies
in high pressure situations?