I uploaded a contract yesterday, just standard
stuff, really, a service agreement. And I needed
to check one thing, a specific liability cap.
So I asked the AI and it came back instantly.
It cited the clause perfectly. Paragraph four,
section B said liability is capped at two times
the total fees paid. It looked professional,
totally sound. Let me guess. There was no Section
B. There was no Section B. The clause didn't
exist at all. But here's the kicker. It wasn't
a glitch. It wasn't broken. The AI, it was trying
to be helpful. And if I hadn't gone back to the
PDF to double check, which, you know, I really
didn't want to do, that helpful little hallucination
could have cost us thousands. And that right
there is the entire paradox we're living in now.
Welcome back to the Deep Dive. It is Monday,
February 9th, 2026. Usually we're around here
talking about speed, you know, how fast the new
models are. But today we're hitting the brakes.
We're talking about trust. We're exploring the
nature of truth when you're dealing with a machine
that is literally programmed to please you. It's
a fascinating problem, isn't it? Because the
models are so much more powerful now than they
were back in, you know, 2023. But that confident
guessing issue, it's still here. And it's almost
worse because the hallucinations sound so...
Convincing now. The grammar is perfect. He wears
a suit and tie now. Exactly. So to navigate this,
we're digging into a technical guide by Max Am.
It's called Why ChatGPT, Gemini, Claude are Still
Hallucinating and How to Fix It. And the core
idea is that we need to change how we work with
these tools. to move from just a casual chat
to something ann calls auditability auditability
that is the skill for 2026. seriously it's not
about finding an answer anymore the machine does
that it's about proving the answer is real okay
so let's start with the why Why does it lie?
I think a lot of us still assume that if it sounds
smart, it must be right. Right. But that's the
wrong frame. It isn't lying out of malice. It
isn't even broken. The irony is it's lying out
of kindness or what it thinks is kindness. These
models at their core, they're just prediction
engines. They're trained to give you the most
likely next word that will satisfy you. So its
goal isn't be truthful. It's complete the sentence
in a pleasing way. Don't disappoint the user.
That's the core directive. So when it scans a
file you upload and it can't find the answer.
It has a choice. It can say, I don't know, which
feels like a failure to its reward system. Or
it can switch gears. It switches from retrieval
mode to generative mode. It just starts guessing
to fill the gap. The guide has this terrifying
example with Apple's Q2 revenue. Walk us through
that because it's not just a wrong number. It's
like a whole work of fiction. It's the classic.
polished lie. So you upload a dense financial
report. You ask, what was Apple's Q2 revenue?
But let's say the document doesn't use that exact
phrase. Maybe it says second quarter fiscal results.
So the simple text search fails. It fails. But
instead of saying, I can't find that, the training
kicks in. It starts thinking, well, Apple's a
big company. Q2 is usually around March. And
it just constructs an answer. In the example,
it says $95 .4 billion for the fiscal 2025 second
quarter, ending March 29, 2025. That is so specific.
It has a dollar amount, a decimal, a date. And
every single piece of it is hallucinated. It's
just a statistical guess of what an earnings
report should look like. You can immediately
see the danger zones and the list of few like
invoicing. Oh, that's a huge risk. You have automated
systems processing invoices and the AI just hallucinates
a line item for a shipping fee because it thinks
there should be one. And you just overpay. By
thousands. And no human would ever catch it because
it looks normal. Insurance is another one. That's
the nightmare. You ask, am I covered for flood
damage? It sees a water damage clause and says,
yep, you're covered. Then the flood hits and
you find out your policy specifically excludes
rising water. The AI just gave you a standard
answer that wasn't true for you. And contracts,
like my story. Miscompliance, wrong dates. It's
the halo effect of good presentation. We see
polish and our brain automatically assumes it's
true. I have to admit, I still struggle with
that. The blind trust. When the output looks
that professional, the formatting is clean, the
grammar is perfect. Yeah. It is just so hard
for my brain to stop and say, wait a minute,
is this real? It's a cognitive bias. We're wired
to believe that competence in form means competence
in fact. But with AI, those two things are totally
decoupled. So this is the big question then.
If the machine prioritizes being helpful over
being truthful. how do we force it to care about
the truth? We have to change the instructions.
We have to make, I don't know, an acceptable,
even a rewarded answer. And the guide says there
are three layers to this. The hardware, the software,
and the verification. Okay, let's start with
layer one, the hardware. Model selection. This
seems basic, but the guide says most people mess
this up right at the start. Yep. They fail before
they even type a word. They just open ChatGPT
or whatever and use the default model that loads.
I'm guilty of that. I just open the window and
go, I assume it's the smartest one. And for serious
document work in 2026, that's a critical mistake.
The default models, the turbo or flash versions,
they're built for speed. For conversation. Right.
They are not built for deep forensic analysis.
You have to manually toggle on the reasoning
capabilities. So we're talking about specific
settings, not just using GPT -5. Exactly. For
ChatGPT, the... For Claude, it's Opus 4 .6 with
extended reasoning on it. And for Google, Gemini
3 Pro. What's the actual difference? Is it just
slower? It is slower, but that's the whole point.
The default model is like an improv comic. Fast,
creative, good at making connections. But you
don't want an improv comic doing your accounting.
You want the deep thinker. The part of the brain
that checks its work. That's what high reasoning
does. It forces the model to spend more compute
cycles on a chain of thought before it gives
you an answer. It has an internal monologue.
It's checking its own work before it speaks.
Precisely. So does picking the right model solve
the whole problem? If I just switch to Opus 4
.6, am I good to go? Nope, absolutely not. That's
just the baseline. It gets you a smart auditor,
but even a smart auditor needs clear instructions.
And that brings us to layer two. the software
the prompts this is where the guide introduces
the grounding complete template right which it
sounds very official it is and it works this
whole section is about giving the ai permission
to fail permission to fail i love that okay so
what's the first prompt prompt one is the grounding
rule you say base your answer only on the uploaded
documents Nothing else. Nothing else. That's
the key part. That little phrase does so much
work. It stops the model from using its vast
internet training data. It shrinks its universe
down to just the PDF you gave it. Okay, prompt
two. This one tackles the people pleaser problem.
Right. This is where you say, if information
isn't found, say, not found in the documents,
don't guess. It seems so simple. You just have
to tell it not to guess. You have to be that
direct. You're overwriting its core training.
And what's wild is that this isn't some clever
hack. Anthropix's own API docs recommend this.
Oh, really? Yeah. They basically say if you want
accuracy, you have to tell the model it's okay
to be silent. So it's a manufacturer -approved
fix. Okay. And prompt three. Demand citations.
For each claim, cite the specific location, document
name, page section, and relevant quotes. Show
me the receipts. Always get the receipts. This
forces it to be a data auditor, not a creative
writer. If it can't point to the line on the
page, it can't make the claim. The guide also
has a couple of bonus prompts. One is the middle
ground. Yeah, that's where you ask it to mark
things it's unsure about as unverified. It's
great for summaries of long reports where you
just need to know where the shaky ground is.
But then there's the nuclear option for high
stakes stuff. The high stakes mode. The prompt
is only respond. If you're 100 % confident. Whoa.
That feels intense. It is. And your answers will
be much shorter. You'll get, I don't know, a
lot more. But the answers you do get will be
rock solid. Imagine the discipline required to
stay silent unless you are 100 % sure. That's
a standard most humans can't even meet. It is,
but that's the choice. Do you want a chatty assistant
or a rigorous auditor? So we have the right brain,
the right instructions. Do we trust it now? Are
we finally done? Not yet. Even with all that,
you can't blindly trust it. You need an independent
audit. Okay, we're back. We've picked a reasoning
model. We've used the grounding prompts. But
the guide says we're still not done. We have
to verify. Trust, but verify. But the cool thing
about doing this in 2026 is that verify doesn't
mean you have to reread the whole document yourself.
Right, because that would defeat the whole purpose.
Exactly. The new concept is AI checking AI. I
like that. A digital second set of eyes. So what's
method one, the low intensity version? The self
-check. This is the easiest one. You just stay
in the same chat window and ask. Rescan the document.
If you can't find the quote, take the claim back.
Does the word race scan actually do something
special? It does. It's a critical word. It forces
the model to perform a new methodological review
instead of just, you know, confirming its own
previous answer. It avoids confirmation bias.
Okay, so that's the quick check. Method two is
the multi -model check. This is your medium intensity
option. You take the output from, say, ChatGPT,
and you feed it, plus the original file, into
a different model, like Claude Opus 4 .6. And
you ask Claude to grade ChatGPT's homework. Basically,
yeah. And the analogy in the guide is perfect.
It's like getting a second opinion from a doctor
who went to a different medical school. They
have different training, different biases. The
chance that they will both make the exact same
weird mistake is incredibly low. That makes a
lot of sense. Okay, then we have the heavy hitter,
method three, notebook LM. This is the gold standard
for this kind of work today. Google's notebook
LM running on Gemini 3. I've used it for research.
Why is it considered the highest intensity check?
Because it was purpose built for this task. It's
not a chat bot trying to be an auditor. It is
an auditor. You upload your document and the
AI's analysis and you ask which claims in this
analysis are not supported by the source document.
And here's the killer feature. It links every
single claim. It verifies directly to the source
text. You can click a little citation number
and it takes you to the exact paragraph in the
PDF. So it's not just telling you it's true.
It's showing you the proof. It creates a verifiable
paper trail. That's the definition of auditability.
This whole system sounds incredibly thorough.
Hardware, software, verification. But I have
to ask, is there anything this system cannot
catch? Is there still a gap where you need a
human? Yes, a big one. It can't fix a bad source
file. And more importantly, it can't understand
human risk. So let's talk about that, the reality
check section. What is this grounded mode not
good for? Well, first, it's not magic. If your
PDF has missing pages or the scan quality is
terrible, the AI can't fix that. The guide has
a great rule of thumb for this. If a junior analyst
couldn't answer it, neither should the AI. Exactly.
Garbage in, garbage out. But the much bigger
limitation is risk assessment. The AI can extract
a liability clause perfectly. It can tell you
verbatim liabilities capped at $5 ,000. But it
has no idea if signing a contract with that clause
is a terrible idea for your business. Right.
It can read the map perfectly, but it can't tell
you there's a cliff at the end of the road. That
takes a lawyer. That takes context about your
business, the market, your negotiating power.
The AI is an extractor, not a strategist. The
guide also mentions math. I feel like we keep
hearing AI is getting better at math, but it's
still a weak spot. With dense tables, yeah. It's
the column confusion problem. It still confuses
totals and subtotals. It skips footnotes. It
basically treats numbers like words. So what's
the fix? Don't use it for math. You slow it down.
You ask it to first reproduce the table exactly
as it sees it in the chat. Once you confirm it,
read the numbers right, then you ask it to do
the math. Show your work. It's like we're back
in fourth grade math class. Always make it show
its work. And the final limitation is creativity
versus precision. Models just love to paraphrase.
It's in their nature. But for legal or compliance
work, similar is not good enough. You need the
exact words. So you have to explicitly command
it. Quote verbatim. Do not paraphrase. You have
to be that blunt. So if we zoom out on all this,
it really sounds like the goal isn't to replace
the human. It's just changing the human's job
description. 100%. The human is no longer the
researcher digging in the file cabinet. The human
is the auditor, the one checking the citations,
assessing the risk, and making the final judgment
call. It's a real shift from finding answers
to verifying truth. And that shift is everything.
It's what separates people who get value from
AI from those who just get noise. Okay, let's
recap the big idea. If someone listening has
a stack of documents on their desk for tomorrow
morning, what is the simple playbook? Three steps.
One, pick a reasoning model. Don't use the default.
Use GBT 5 .3 or Opus 4 .6 with the reasoning
modes on. Two, ground it. Paste in that template.
Tell it to only use the file and give it permission
to say, I don't know. And three, verify. Use
a second AI or ideally notebook LM to cross -check
the key facts. It sounds like a little bit of
extra work upfront. It's maybe 30 seconds more
per task. But it saves you hours of panic and
cleanup later. Trust but verify. An old saying,
but it feels more important now than ever before.
It's the only way to operate in an age of infinite
confident information. So here's the challenge
for you listening. The next time you upload a
file, don't just ask it to summarize. Try that
grounding template. And just watch how different
the output looks when the AI stops trying to
guess and starts citing its sources. It gets
remarkably quiet. And that quiet, that's the
sound of accuracy. I love that. Thank you for
deep diving with us today. We'll catch you on
the next one.