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When a retrieval-augmented generation (RAG) system gives wrong or thin answers, the vector database is usually the first component people inspect, and it is often not where the failure started. A 2025 arXiv study by Leopold Müller, Joshua Holstein, Sarah Bause, Gerhard Satzger and Niklas Kühl, titled Data Quality Challenges in Retrieval-Augmented Generation, places data-quality problems across four processing stages: data extraction, data transformation, prompt and search, and generation. The index is one part of the search stage. In most failing systems, the bad answer is the end of a chain that began with a parser that dropped a table header, a chunker that split a clause from its condition, or a metadata field that was never populated. The vector store still matters for retrieval quality, so the practical advice is about where to look first, not about whether the index can be ignored.
Follow the path from source document to answer
A RAG answer is only as good as the representation of the source material that reaches the model. A useful way to diagnose a system is to walk the same path a document takes:
- Extraction and parsing: converting PDFs, Word files, web pages or database rows into text.
- Transformation and chunk formation: cleaning text, deciding where to split it, and attaching context to each piece.
- Metadata and indexing: storing embeddings, keywords, dates, owners, versions and access labels alongside each chunk.
- Query-time search and ranking: turning a question into a search, filtering candidates and ordering them.
- Generation and answer evaluation: giving the retrieved evidence to a language model and checking whether the answer stays faithful to it.
At every step, the useful question is the same: does the representation still contain the information and context the user’s question needs? If it does not, no later component can recover it reliably.
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The Müller and colleagues study is based on 16 semi-structured interviews with practitioners. From those interviews, the authors derive 15 distinct data-quality dimensions across the four RAG processing stages. These are counts from that one study, not estimates for the population of RAG teams. The paper’s abstract reports that data-quality dimensions are concentrated in the early stages of the pipeline and that issues can transform and propagate as they move through it. That second point is the one that changes debugging: a problem introduced early does not stay where it was introduced.
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Data extraction
Extraction is where text is separated from its layout. Common losses include table cells detached from their row and column labels, footnotes merged into body text, headers and page numbers repeated inside chunks, and scanned pages with character-recognition errors. Illustrative symptom: a retrieved passage contains the right figure, but the label saying whether it is a forecast, a prior-year value or a percentage has been lost.
Data transformation and chunk formation
Transformation covers cleaning, normalising, splitting and enriching text. Failures here include splitting a sentence from the condition it depends on, cutting a table in half, dropping the section heading that gives a paragraph its meaning, and indexing several versions of the same policy without a version label. A chunk can be semantically similar to a question and still be wrong for it, because the context that distinguishes the correct version was removed at this stage.
Prompt and search
At query time, the system turns the user’s question into a search. Problems include a question phrased with different terms from the source text, filters that exclude the right document because a date or department field was empty, and a ranking that places a near-duplicate ahead of the authoritative version. A vector-only search can miss exact identifiers such as product codes or clause numbers, which is one reason keyword retrieval is often combined with it (discussed below).
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Generation is the stage most often blamed. A model may state something the retrieved text does not support, or it may answer from partial evidence without signalling that information is missing. These failures can be real generation faults, but they are also common when the evidence passed in was incomplete or misleading, which is why generation should be evaluated against the context it actually received.
Why errors propagate instead of staying local
Consider a supplier contract where the payment term is stored as “30” in a table cell, and the column header “days” was lost during extraction. The chunk is indexed and retrieved for a question about payment terms. The model reads “30” and answers “30 days”, which may be right, or may be wrong if the value was “30 days after invoice receipt” and the condition was in the header row. Looking only at the vector store, the retrieval appears to have worked: the right chunk was returned. The fault lies upstream, and only a comparison against the original document reveals it.
This is the practical meaning of the propagation finding. Tuning the embedding model, changing the index or increasing the number of retrieved chunks can make a pipeline look different without repairing the content that is being retrieved.
Structured and semi-structured enterprise data
Enterprise data is often a mixture of prose, spreadsheets, database exports and tickets, and text-only assumptions break down quickly. A separate paper on structured enterprise and internal data describes a proposed framework that combines several methods:
- dense retrieval together with BM25, a keyword-based ranking method, so that both meaning and exact terms can contribute;
- metadata-aware filtering, so that candidates are restricted by fields such as department, date or document type before ranking;
- reranking of the candidates that the first search returns;
- semantic chunking, which splits content according to meaning rather than fixed length;
- preservation of tabular row-column integrity, so that a value stays linked to its row and column labels.
These are components of that paper’s proposed framework. They are not presented there as universally required parts of RAG, and the paper does not establish them as independently verified production results. They are useful as a checklist of capabilities to test against your own data. A quick test is to ask a question whose answer depends on a row label and a column label, then confirm that the returned context includes both.
Chunking should follow structure only where structure carries meaning
A paper on financial reports studies document-element-based chunking, which splits content along the elements of the document such as headings, tables and sections. Its argument is that paragraph-level chunking can miss structural information that the reader would use to interpret a passage. That is a sound reason to keep headings and table captions attached to their content. The finding is scoped to financial-report documents, however, and should not be read as proof that structure-aware chunking is better for every corpus. A set of short support articles or a collection of meeting notes may gain little from the same treatment.
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For any corpus, a simple check is to open a sample of stored chunks and ask whether each one, read alone, identifies what document it comes from, which section it belongs to, and which version applies.
Measure retrieval and generation separately
An end-to-end score can tell you that answers are poor without telling you why. The RAGChecker framework proposes fine-grained evaluation that scores retrieval and generation separately, along with claim-level checks against reference text, so that a wrong answer can be traced to a specific stage. The three outcomes that matter most are listed below.
| Outcome | What it looks like | Where to investigate first |
|---|---|---|
| Weak evidence retrieved | The returned chunks do not contain the information needed, or contain it only in a distorted form | Extraction, chunking, metadata, query-time search and ranking |
| Unsupported claim generated | The answer asserts a fact that no retrieved chunk states, or contradicts one | Generation, prompt instructions, and whether the context was complete |
| Relevant information omitted | The answer is accurate as far as it goes but leaves out a condition, exception or second source | Ranking depth, chunk boundaries, and whether related passages were filtered out |
Claim-level checking is more work than a single rating, but it is the only way to see whether an answer was wrong in one sentence or throughout.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.A diagnostic order for a failing system
The following sequence is editorial guidance built on the stage-based lens above. It is not a verbatim procedure from the cited studies.
- Collect a set of questions that the system answers badly, ideally 20 to 50, and record the expected answer and its source document for each.
- For each source, compare the stored text with the original. Check tables, headers, footnotes and dates. Fix extraction before anything else.
- Open the chunks that should answer each question. Confirm that each one carries its heading, document identifier, version and any filter fields the query depends on.
- Check whether the correct chunk appears in the top results for that question. If it does not, examine the query terms, filters and ranking. If you use vector-only search, test whether a keyword or hybrid approach changes the result.
- If the correct chunk is returned, check each claim in the generated answer against the retrieved text. Unsupported claims point to generation or prompting. Missing conditions point back to chunk boundaries.
- Change one component at a time and rerun the same question set, so that improvements can be attributed to a specific stage.
Limits of the current evidence
- The interview-based study describes practitioner experience through a small set of interviews. It does not measure how common each failure is across RAG deployments.
- The enterprise-data framework and the financial-report chunking study each address a specific corpus type, and neither establishes a general performance gain for every RAG system.
- The thesis that data quality drives RAG outcomes does not mean vector databases are unimportant. Index choice, embedding models and search parameters can change results, and they should be tested on the same question set once the content is sound.
The strongest supported conclusion is narrower but still useful: before replacing the vector store, confirm that the content entering it is complete, correctly labelled and structurally intact, and measure retrieval and generation separately so the fault can be located.
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