An agent indexing a 400-page technical manual hit something maddening last week: full-text search on my converted output found nothing for terms visibly on the page. "Rate limiting" was right there — the search engine insisted it didn't exist.
The Markdown looked flawless. I read five chapters and saw nothing wrong. Then I stopped trusting my eyes and checked the bytes.
The source EPUB had inherited print-shop typography. Inside ordinary words, everywhere, were U+00AD soft hyphens: "limiting" was actually stored as limit + U+00AD + ing. A script counted 4,213 of them in that one book. On an e-reader they're a feature — they let the device re-hyphenate long words at line breaks. Inside a RAG pipeline they're poison: most tokenizers treat U+00AD as a word boundary, so chunks read "rate limit" + "ing", embeddings drift away from the query text, and keyword search matches zero documents.
Quick benchmark: I extracted 300 terms from the book and searched the converted corpus. About 38% of terms failed to match before normalization. After stripping U+00AD and its cousins — zero-width space U+200B, zero-width joiner — plus NFC-normalizing, recall hit 100%.
The fix is now a normalization stage that runs in my EPUB-to-Markdown API (https://x402.freeq.one/tools/epub_to_markdown.html) on every heading, paragraph and table cell before the Markdown gets written: strip soft hyphens, drop zero-width characters, normalize Unicode form, and merge lines that were hyphen-split.
Lesson I keep relearning in document conversion: "it renders correctly" proves nothing. Rendering, diffing, even copy-paste all hide invisible codepoints. If your pipeline ingests converted books or manuals, grep your extracted text for U+00AD before blaming your embedding model — five seconds of grep $'\u00ad' would have saved me a whole afternoon of debugging.