What this is about. This spring a machine translated my blog into Swedish. This autumn I read the result. Yeah I know, kinda lazy. So much time and so little to do... It translated a product name into a day of the week, and my install commands into Swedish.

Why. Generic AI is good at generic questions. The moment it has to work inside your business, in your voice, with your products and your rules, it falls over, and it looks like an AI problem. It's a context problem. That's good news, because context is something you own. You can has fix!

The Swedish was crap

Most of my older posts have Swedish versions, made in March by the translation service I run next to my CMS. In October I asked my AI to review the old posts and redo the Swedish, because, in my words:

The one I came over is crap.

That turned out to be generous. Some sweet highlights:

• Monday.com became Måndag.com. Lovable, a Swedish company, became Lövbar, which means "leafable".

• pip install -r requirements.txt became pip installera -r krav.txt. Poetry, the Python tool, became Poesi. Go, the language, became Gå, the verb.

• "Execution", as in running a task, became avrättning. That's the kind with a blindfold and an axe.

• "Data residency laws" became laws about residence permits.

• "$5.55B" became 5,55 dollar. A rounding error of about five billion.

The German was no better. "Digest" became Verdauungstrakt, the digestive tract. Hilarious!

None of this is the model being stupid. Every one of those words was translated correctly, as a word. What the machine didn't know was that Monday.com is a product, that a line in a code block is something you type, and that my post was about servers, not prisons. Language it had plenty of. What it was missing was the context.

Context is more than documents

When people say "give the AI your data" they usually mean documents: the manual, the product catalogue, the FAQ. That's the part everyone has heard of. Look it up before you answer is the whole idea behind what's called RAG: an AI that reads your manual before it opens its mouth. It's a good idea, and I wrote about building one in my Local AI series.

The translations taught me there are other kinds, and they're easier to miss.

What your words mean. "Make" is a verb, unless it's the automation tool. "Execution" is a word, unless you're a developer. A style guide or a glossary of your products, your tools and your no-translate words is context too, and it's usually the cheapest kind to write down.

Which version is current. Several of my Swedish posts were translated from an older English version. The English had moved on and the Swedish was still quoting lines I'd deleted. The AI had read the manual but it was last year's manual! Today, an hour after the new Swedish went in, I edited two English posts in the CMS. The check we'd built refused to write the next Swedish update because the page structure no longer matched. That refusal was the most useful thing it did all day.

Whose corrections count? My translation service keeps the corrections I make by hand, as examples to learn from. A good idea, with two traps in it. First, the obvious way for my AI's script to save its new Swedish was the same door my corrections go through, and everything through that door is filed as "a human fixed this". That would have taught the machine to imitate a machine. The script uses a door that doesn't record anything. Second, some of my own "corrections" from March weren't corrections at all: I'd added lines in Swedish that never existed in English. As training data, that teaches a model to invent. Read that last sentence aloud!

The same wall in a shop or a course

My translations are a low-stakes place to hit this wall. Here's where it costs money.

A webshop. Ask a generic AI to write a product description and it writes a nice one, for a different brand. It doesn't know that your T-shirts are printed on demand, that you never say "premium", or that returns take fourteen days, not thirty. A support bot without your shipping policy gives a confident, wrong delivery date, and your customer remembers the date, not the bot. Give the same model your catalogue, your policies and ten descriptions you're proud of, and the copy sounds like you.

A course. A student asks a question about lesson four. A generic model answers from the internet, which is fine until it contradicts your lesson. One that reads the course material first answers from what you taught, and can point to the page. The good version also knows which edition of the course the student is on, and that's the stale-version problem again.

In both cases the model is the cheap part. Context is the work: deciding what goes in, keeping it current, and knowing which corrections came from a person.

What happened, and how else it could be done

The machine translations went wrong because the machine had my words and none of my context. The fix was the boring kind:

• My AI translated from the live English page, not from a copy.

• Code, links and product names were left alone.

• A script compares the translation with the live page and refuses it when the structure differs.

• The old versions are kept, so I can roll any post back.

Running it yourself matters more than it sounds

Once the AI reads your price lists, your policies and your customers' questions, that context is the most valuable thing in the system. I'd rather keep it next to the rest of my stack than mail it to someone else's model every time I ask a question.

Before you blame the model, check these things:

1. Does it know your names, products and no-translate words?

2. Is it reading the current version of your material?

3. Can you tell which corrections came from a person?

4. Is anything checking its output against the source?

The Swedish is fixed. Lovable is Lovable again, and no one gets executed in my Docker posts any more. The old Swedish is still saved, in case I ever want a laugh.

Good luck,
//T