Prepare and chunk source documents
Turn source files into clean, traceable passages suitable for search.
- Explain why document cleanup, chunk boundaries, and source metadata matter.
Before indexing, extract readable text and preserve useful structure such as headings and source identifiers. Split long documents into chunks that are small enough to retrieve precisely but large enough to retain meaning. Overlap can preserve context across boundaries, at the cost of additional duplicate text.
A small example
1paragraphs = ["Install the package.", "Then set the API endpoint."]
2chunks = [" ".join(paragraphs)]
3for number, chunk in enumerate(chunks, start=1):
4 print(number, chunk)1 Install the package. Then set the API endpoint.
Chunking is a tradeoff, not a universal fixed size. Splitting in the middle of a table row or procedure can remove meaning. Keep metadata such as document title, section, and access scope with each chunk so results can be cited, filtered, and audited later.
Key takeaways
Explain why document cleanup, chunk boundaries, and source metadata matter.
Check that retrieved evidence is relevant, current, and allowed for this user.
Lesson quiz
5 questions · pass with 4 correct · up to 50 XP
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Questions about this lesson
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