Canonical: https://hypler.com/wiki/retrieval-source-quality

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# Retrieval Begins With Source Quality

Retrieval is not a substitute for data stewardship. It depends on source identity, freshness, provenance, and an interface that can show uncertainty.

## Know what the source can support

A retrieved passage is useful only in relation to its source. Teams need to know where a record came from, when it was observed, how it was transformed, and whether it is complete enough for the current decision. Without that context, a relevant-looking result can receive more confidence than the source deserves.

Source-aware systems keep observed content separate from derived summaries or model output. That distinction lets an interface show whether it is presenting a direct record, a normalized field, or an inference. It also gives reviewers a path back to the underlying evidence when a result is disputed.

- Preserve source identity and observation time where practical.
- Document normalization rules and material transformations.
- Expose unavailable, stale, and conflicting states instead of hiding them.

## Retrieval needs an interface contract

A retrieval layer should state what a caller receives: identifiers, excerpts, source references, freshness metadata, and limits. It should also define which failures are distinguishable. No results, source unavailable, and search timed out are different conditions with different next steps for a person or downstream system.

Ranking can help people discover relevant material, but ranking is not verification. A system should avoid presenting the highest-ranked result as a definitive answer when the source is incomplete or the question requires a human judgment. The best result may be a cited uncertainty or a request for a narrower question.

- Return citations or stable source references with useful results.
- Keep retrieval ranking separate from approval or truth claims.
- Test empty, ambiguous, and conflicting-source queries.

## Quality improves through review loops

Teams can improve retrieval by collecting representative questions, inspecting the source material returned, and recording the cases where a result was misleading or insufficient. That corpus becomes a practical evaluation set for retrieval configuration, chunking choices, metadata, and user interface states.

The goal is not to make every answer automatic. It is to give a user enough provenance and context to decide whether a result is usable, needs corroboration, or should remain unresolved.

## Sources

- [Hypler SI information](https://hypler.com/ai-information)

## Related capabilities

- [Data integration engineering](https://hypler.com/data-integration-engineering)