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An agent that retrieves the right document can still attach words from a different document or a different revision. Checking that the quoted text actually appears in the cited copy is the only way to close that gap, and it is the safeguard Archana describes in her 2026 DEV Community article about TSB Oracle, a project that answers car-repair questions from public records.
The failure mode: the right document with the wrong words
Retrieval answers the question “which document is relevant?” It does not answer “did this sentence come from that document?” A model can retrieve a real service bulletin, summarize it correctly in its own words, and then attach a quotation it pulled from a neighboring record, an older revision, or its own memory. The citation looks legitimate because the document exists and is on topic.
Archana’s project treats that as a separate problem with a separate check. Every quoted phrase is compared against the dataset’s copy of the specific document the answer cites. If the words are not in that copy, the quote is not allowed to pass as sourced.
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As described in the article, the pipeline runs in this order:
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- Attach an identifier to every factual sentence. Each claim carries the NHTSA identifier it came from, so a reader can trace it back to a record.
- Match quoted text to the cited document. The quoted words are searched inside the stored copy of the document named in the citation, not in the whole corpus. A match in some other document does not count.
- Mark failures in the answer. A quote that fails verification stays visible and is flagged in the answer. The author says it is not silently withheld, so the reader can see that the wording could not be confirmed.
- Surface disagreements. When two records conflict about the same vehicle, both positions are presented rather than merged into one confident answer.
The point of step two is narrow. The check confirms that the words exist in the cited copy. It does not, by itself, confirm that the cited copy is the right revision for the reader’s vehicle. That question is handled elsewhere, and it is where the project’s known weakness sits.
Where the check is weaker: overwritten documents and missing hashes
Archana states a limitation that matters for anyone copying this design. The importer overwrites each document in place and stamps a retrieval time. The quote check compares against the current stored copy and does not consult Sanity’s document history. If a source is revised, the earlier wording is no longer available to the check.
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She describes a content hash, stored with each imported text, as stronger evidence of the exact version that was checked. The design as described does not keep such a hash for each quote, so a passing check establishes a match against the current stored copy, not a durable record of which text version was verified on a given date.
For a system where revisions matter, that is the first thing to fix. Store a hash of the imported text alongside the retrieval time, and record the hash in the answer next to the quote, so a later revision cannot silently change what a past answer was checked against.
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Separating prose retrieval from structured applicability
The project does not ask one search mechanism to do everything. Two kinds of question are handled differently:
- Wording questions (“what does this bulletin say about braking?”) use Sanity Context, which retrieves relevant text.
- Structured questions (“does this document apply to a 2019 model with this trim?”) use GROQ queries against the content types, such as model year and trim fields.
This split matters because a paragraph can be semantically close to a question and still apply to the wrong model year or trim. Year and trim are fields to filter on, not sentences to match.
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Content types in the dataset
The Sanity schema separates source documents, claims, contradictions, and decisions. Keeping these apart makes it possible to ask which claims rest on which source, which sources disagree, and which resolution a person has approved. The article describes these types as the structure behind the answers; it does not present a published schema or benchmark.
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Disagreements and human review
When records conflict, the project records a proposed resolution, but the author says a person reviews that proposal before it becomes the settled answer. That is a deliberate choice. A system that resolves conflicts automatically can look authoritative while hiding a judgment call, especially for safety-related topics.
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Worked example: the CR-V braking question
The example dataset covers unexpected automatic emergency braking on 2017 to 2022 Honda CR-Vs. Archana’s summary of the records is that NHTSA investigation EA24-002 covers those model years, while Honda software update 26-091 stops at 2019 and excludes the LX trim. The example question an owner might ask is: “My CR-V brakes hard on its own with nothing ahead. The dealer says that’s normal. Is there a fix?”
This is the author’s reading of the records, not repair guidance. The gap between a broad investigation and a narrower update is exactly the kind of mismatch a model can blur. An owner should confirm applicability against the NHTSA record and the Honda bulletin for their own model year and trim, and against their dealer’s documentation, before assuming a remedy exists.
Data-quality findings reported by the author
Building the knowledge base surfaced four issues in NHTSA summaries. Archana gives terminology errors and count conflation as examples. The two count examples below are her figures from her project; they have not been checked against the agency’s published totals here, and they should be re-derived from the source records before being reused.
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The lesson is about counting, not about the vehicle. A total drawn from all reports and a total drawn from complaints measure different populations, and a summary that mixes them will overstate or understate the problem.
Checklist for evaluating a source-grounded agent
- Does every factual sentence map to a source identifier?
- Is each quotation checked against the exact cited document, not the corpus as a whole?
- Are document revisions or content hashes retained, so a past check can be reproduced?
- Are structured fields such as year, model, and trim filtered separately from prose retrieval?
- Are disagreements shown to the reader instead of silently resolved?
- Does a person approve any resolution that changes the answer?
The article describes these design choices for its own project. It does not benchmark TSB Oracle against other tools, so the checklist is a way to compare designs, not a ranking.
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