A couple of weeks ago I dropped the CRA text (the EU's cybersecurity regulation for IoT devices) into ChatGPT and asked when the main requirements actually kick in. The answer was confident and wrong - it mixed up the date the regulation entered into force (2024) with the date the requirements actually apply (2027). Three years off, stated like an obvious fact.
My team (Platanor, embedded security for IoT) has been building an internal reference on CRA/RED/NIS2/CSA for a few months now, and this is exactly the kind of mix-up we kept running into whenever we just threw the regulation PDF at a model.
The problem isn't the model. It's how the source is laid out: dates are scattered across different articles with no explicit link between them, token-based chunking cuts sentences off mid-article, and the model has no way to tell how fresh the text is.
When we rebuilt the base as a public repository, we fixed this with file structure, not prompting.
Cut by article headings, not by tokens:
### Article 13
Obligations of manufacturers
1. When placing a product...
### Article 14
Reporting obligations...
### Article N is a natural boundary. Each chunk stays whole - the article never gets split mid-sentence.
Source priority, written into the file itself, not the prompt:
primary source > official related documents > third-party summaries > our own analysis. The model sees this right next to the content, not as an instruction that's easy to lose in a long chat.
A verification date on every file:
> Last verified: 2026-08-10.
> Annex I application deadline: 11 December 2027 (not to be confused with the entry-into-force date - 10 December 2024).
That one line is what removed the exact error I opened with.
llms.txt at the repo root - an index of every file, so an agent can pick what to load instead of reading the whole repository.
The same questions now get answered correctly - not because the model got smarter, but because the source stopped being one continuous wall of text.
We packaged the whole approach, plus the fact-checked base on CRA/RED/NIS2/CSA, into one repository - pull it into your own RAG pipeline or install it as a Claude Skill: https://github.com/Platanor/hardware-compliance-handbook
