Bolds the start of each word so you can scan the text faster.
Theme
Language
App
Understanding something complicated
~ min read
📋30-second summary
The documents that show up in your mailbox are written in bureaucratese: the AI is excellent at translating them into normal language.
Three techniques cover almost everything: explain it simply, extract just the data I need, tell me what to watch for.
For contracts the third technique is the most useful: it helps you spot risky clauses before you sign.
Numbers are the AI’s weak spot: translation is its job, arithmetic stays yours.
What you paste in (a bill, a contract, a medical report) contains your personal data. Know it before you do it.
You opened an electricity bill and got stuck on the line “system
charges adjustment factor”. You read a contract and didn’t
understand what “automatic renewal unless cancelled within thirty
days of expiry” actually means. Or you got a medical report with
acronyms that look like military codes. It’s happened to everyone.
The problem isn’t your attention. It’s that these documents are
written to protect the people who send them, not to make sense to
you. The conversational AI is excellent at one thing: taking a
difficult text and giving it back to you in normal language. It does
the same job on contracts, condo bylaws, tax authority letters, drug
leaflets, insurance policy terms. It’s one of the most useful
applications in everyday life.
In this lesson we’ll see three techniques that work almost every
time, with a real example on an electricity bill. About medical
reports, a heads-up before going further: the AI is a good translator
of acronyms and clinical terms, not a substitute for a doctor in
choosing what to do. We’ll talk about it in the last lesson of the
module, What not to do.
The base technique. You paste (or describe) the difficult text and
you ask the AI to explain it in simple words. It works on any
document: a contract clause, a page of a regulation, a paragraph of
an article. The AI strips out the jargon and gives you back the same
information in plain language. You decide the level: “like I’m ten”
is the extreme case, but you can also ask “like to an adult who
knows nothing about the topic” or “like to a colleague who knows
half of what I know”. The more precise you are about your starting
point, the better the explanation fits.
“Explain this contract sentence as if I knew nothing about law:
‘In case of early termination, the customer is required to pay the
fee provided in Annex B, paragraph 3, plus administrative
charges.’”
You’ll get an answer like: “if you close the contract before the end
date you pay a penalty, which is written in another sheet of the
contract, Annex B, plus a small handling fee”. Same meaning,
different language.
The difference from the first technique is the focus: there you want
to understand the document, here you only want a piece of data.
Often you don’t need the whole thing explained: you just need a
direct answer to a specific question. When do I have to pay? How
much does it cost to cancel? When does the coverage start? The AI
pulls the data out of the text without making you read twenty pages.
“From this bill, tell me only: total amount, due date, and how
much the fixed monthly fee is.”
The model reads the whole document but only answers what you asked.
It’s the right pattern for long documents (an insurance PDF, a condo
bylaws document, an investment information sheet) when you need a
specific piece of information and nothing more.
The third technique is the most powerful, and it shines on contracts
before you sign them. Instead of asking “explain it” or “extract a
piece of data”, you ask the AI to do the job a knowledgeable friend
would: flag the things that could give me trouble.
“Read this rental contract and tell me which clauses could be
disadvantageous for me as a tenant. Focus on penalties, automatic
renewals, minimum duration, and expenses that might come back to
me.”
The AI doesn’t sign anything for you, but it pulls out a list of the
clauses to slow down on: automatic renewal, non-refundable deposit,
binding minimum duration, penalties hidden in the footnotes. On a
rental, a typical answer sounds like: “24-month minimum term with a
240-euro penalty if you leave early; automatic renewal unless you
cancel three months before expiry; extraordinary maintenance is on
the tenant, this is a clause to verify against local law.” It’s
useful when you’re about to sign a phone offer, an electricity
contract, a gym membership, an insurance policy, a real estate
preliminary contract. It gives you a second pair of eyes before a
decision that locks you in.
The first two techniques at work on a real scene: a bill that came
in higher than usual, and you want to understand why.
👤
You
Explain this electricity bill. Total amount 142.30 euros for November. The line items are:
- Energy commodity charge: 58.40
- Network and metering fee: 27.80
- System charges: 19.50
- Taxes (excise + VAT): 22.10
- Adjustment from previous billing: 14.50
What exactly am I paying for, and why is there that adjustment?Explain this electricity bill. Total amount 142.30 euros for November. The line items are:
- Energy commodity charge: 58.40
- Network and metering fee: 27.80
- System charges: 19.50
- Taxes (excise + VAT): 22.10
- Adjustment from previous billing: 14.50
What exactly am I paying for, and why is there that adjustment?
🤖
AI
I'll go item by item in plain language.
"Energy commodity charge" (€58.40) is what you actually pay for the kilowatt-hours you used. The real "cost of electricity".
"Network and metering fee" (€27.80) is what you pay the grid operator to bring electricity to your home, plus the meter reading.
"System charges" (€19.50) are costs the State puts on every bill to fund things like incentives for renewables and social subsidies.
"Taxes" (€22.10) are excise on energy plus VAT.
"Adjustment from previous billing" (€14.50) is the line you're asking about. In an earlier bill the supplier had estimated your consumption, and now they're correcting it because the actual figure (read from the meter) was higher. It's a back-charge, not a new cost: you're paying for energy you'd already used.
To give you the order of magnitude: actual consumption is about 41% of the total. The rest is infrastructure, taxes, and a retroactive correction.I'll go item by item in plain language.
"Energy commodity charge" (€58.40) is what you actually pay for the kilowatt-hours you used. The real "cost of electricity".
"Network and metering fee" (€27.80) is what you pay the grid operator to bring electricity to your home, plus the meter reading.
"System charges" (€19.50) are costs the State puts on every bill to fund things like incentives for renewables and social subsidies.
"Taxes" (€22.10) are excise on energy plus VAT.
"Adjustment from previous billing" (€14.50) is the line you're asking about. In an earlier bill the supplier had estimated your consumption, and now they're correcting it because the actual figure (read from the meter) was higher. It's a back-charge, not a new cost: you're paying for energy you'd already used.
To give you the order of magnitude: actual consumption is about 41% of the total. The rest is infrastructure, taxes, and a retroactive correction.
In four lines of prompt you got a complete translation, and you
identified the culprit of the higher amount (a retroactive
adjustment, not a sudden runaway consumption). You could also have
asked just “why is it high?” (technique 2), and you’d have gotten
a direct answer; or “tell me if anything looks off” (technique 3),
and it would have checked for you. Three different lenses on the
same document.
One thing has to be said clearly: the AI often gets numbers
wrong. It mixes up line items, adds badly, swaps decimals and
thousands, takes a value from one row and assigns it to another. It
happens even on trivial sums: it’ll give you 142.80 instead of
142.30 because it read 27.80 instead of 27.30, and you won’t catch
it unless you double-check. Not because it’s stupid, but because it
isn’t computing on a calculator: it’s predicting plausible text, and
a plausible number isn’t a correct number. You saw the mechanism in
When to trust it (and when
not).
Numbers are the obvious case. The AI can be wrong on the words too:
a clause translated too softly, an exception skipped, a plausible
interpretation that doesn’t actually fit your specific text. The
counter-move is to ask the AI to quote the exact sentence in the
document it drew the explanation from (“which line of the contract
did you get this from?”). If it can’t point to it, or it pulls one
out that isn’t in the text, you’ve found the weak spot.
For the documents in this lesson, the practical rule is simple.
It also applies to dates, percentages, tax IDs, phone numbers.
Anything that’s a number is to be re-checked against the original
document.
Understanding a complicated document is one of the highest-value
everyday uses of the AI. The next lesson scales up: no longer a bill
or a clause, but a long PDF or a whole article. Same spirit, slightly
more structured techniques, and a big note on verification.