Clean the data first: a practical prep layer for agent knowledge bases
Puzzleheaded_Box2842 · reddit · 2026-08-27
The author argues most agent projects hit the same wall: agents answer and act on retrieved context, but raw data is rarely ready. Indexing messy data directly yields incomplete chunks, duplicates, broken tables, stale info and untraceable context—agents look capable but answers stay unstable because the knowledge layer is weak.
A better workflow adds a real data preparation layer before the agent touches the knowledge:
- Parse varied sources into structured text, preserving headings, tables, source IDs and metadata
- Clean noise without altering facts; deduplicate and drop low-value chunks
- Split by source type, not one fixed chunk size
- Anonymize sensitive info when needed
- Generate QA pairs/eval sets for retrieval testing; keep raw and cleaned versions for debugging; make every chunk traceable
Key point: "agent + knowledge base" should not mean plugging a vector DB into a framework—retrieval is often one step in a longer workflow, and bad context corrupts tool choice, reasoning, planning and actions. The author is building OpenDCAI/DataFlow around this direction.
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