Retrieval quality starts before embeddings are created. A PDF parser can lose a table header, a scanned page can have poor text recognition, and a document export can repeat navigation on every page. If these defects enter the index, a better model may only express the wrong evidence more fluently.
A practical scenario
Take a price list with columns for product, region, currency, and validity date. Preserve those relationships during extraction. A chunk containing only the amount cannot support a reliable answer. Compare extracted text against representative pages and treat extraction warnings as review work rather than silently accepting them.
Design the first version
Assign stable document IDs, versions, owners, and access scopes. Detect duplicates before indexing. Keep a record of parsing and chunking settings so failed documents can be reprocessed consistently. Design deletion to remove source text, chunks, vectors, and cached answers where applicable.
What to test and measure
Test scanned documents, empty files, rotated pages, tables, conflicting revisions, and unsupported formats. Measure parsing success and retrieval accuracy separately. When an answer fails, inspect whether the correct evidence survived ingestion before adjusting search ranking. This avoids tuning retrieval around a broken source.
Questions to resolve before commissioning
- Which source system owns the facts used in this workflow?
- Who reviews exceptions and corrects inaccurate output?
- What baseline and acceptance criteria will determine whether the pilot is useful?
- What should the user do when a source, tool, or device is unavailable?
Explore the implementation
This is a planning guide, not a report of measured client results. Examples are illustrative. Explore the related SyntaxLab demo to discuss the interaction, then use your own records and acceptance criteria for a production pilot. Discuss a scoped project or review our AI automation services.
Further reading
Read Microsoft guidance on evaluating grounded AI answers for technical background. Continue with RAG Chunking for Business Documents: Preserve the Rule and Its Exception.