You
have
a
50,000-row
spreadsheet.
The
question
isn’t
whether
you
can
load
it
into
memory
—
it’s
whether
the
LLM
can
actually
understand
what
you’re
asking
it
to
do
with
the
data.
Last
month,
I
fed
a
CSV
to
Claude
and
asked
it
to
find
anomalies.
It
returned
a
summary
that
was
technically
accurate
but
missed
the
actual
spike
I
was
looking
for.
The
problem
wasn’t
the
model
—
it
was
how
I
structured
the
request
and
what
subset
of
data
I
sent.
Setup:
Get
Your
Data
Into
the
Model
Claude
and
GPT-4o
can’t
directly
open
files.
You
have
two
paths:
paste
the
data
directly
or
use
an
API
that
handles
file
uploads.
For
small
datasets
(under
10MB),
pasting
works.
For
anything
larger,
you
need
a
structured
approach.
Method
1:
Paste
Raw
Data
Copy
your
CSV
directly
into
the
conversation.
This
works
reliably
for
datasets
under
100,000
rows
(roughly
50MB
of
text).
Claude’s
context
window
is
currently
200,000
tokens;
GPT-4o’s
is
128,000
tokens.
A
typical
CSV
row
runs
50–200
tokens
depending
on
column
count
and
data
density.
#
Bad
approach
User:
Here's
my
data.
Analyze
it.
[pastes
500
rows]
#
Better
approach
User:
I'm
sending
you
Q3
sales
data:
847
rows,
12
columns
(date,
product,
region,
revenue,
units,
margin,
discount,
rep_name,
customer_type,
payment_method,
delivery_days,
repeat_customer).
Task:
Identify
which
products
have
declining
margins
month-over-month
and
which
regions
have
the
highest
variation
in
delivery
times.
Context:
We
launched
free
shipping
in
August,
so
delivery
times
may
have
changed.
Margins
typically
run
20–35%.
Please
structure
your
output
as:
1.
Products
with
margin
decline
(product
name,
Q2
margin,
Q3
margin,
%
change)
2.
Regions
ranked
by
delivery
time
variation
(region
name,
average
days,
std
dev)
3.
One
anomaly
I
should
investigate
immediately
Notice
the
structure:
what
data
is
included,
the
exact
task,
relevant
context,
and
expected
output
format.
This
cuts
hallucinations
by
roughly
40%
compared
to
