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1Fix the driver behind crashes, sound loss and screen glitches2Repair Windows errors before they cause bigger problems3Scan for outdated or missing drivers - takes under a minuteA stale index does not necessarily cause hallucinations. What it does is hand the model outdated or incomplete passages as grounding, and a model grounded on the wrong passages can still give a wrong answer in confident language. Keeping a web-scraped retrieval index current takes four things working together: finding new pages, noticing edits and deletions, ingesting changed content correctly, and confirming that retrieval actually returns the current facts. A crawl job that finishes successfully confirms only that the fetch step ran.
How RAG divides the work
Retrieval-augmented generation has two stages. Retrieval searches a maintained corpus for material relevant to a question. Generation sends the question together with the retrieved passages to a model, which writes the answer. The index is the organized form of that corpus, built to make retrieval fast. It can also keep page titles and URLs so that an answer can point back to where each passage came from.
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The two stages fail in different ways. Retrieval fails when it returns nothing useful, the wrong passage, or only part of what the answer needs. Generation fails when the model misreads the passages, overgeneralizes them, or fills gaps they do not cover. Freshness problems start in retrieval, because the index is only as current as the last successful ingestion of each source.
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What “fresh” actually means
Freshness is the product of four processes, and each can break while the others keep running:
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- Discovery finds URLs that did not exist at the last run. Sources include seed lists, sitemaps, links on pages already crawled, and connector listings.
- Change detection identifies which known URLs have been modified or removed. Some systems compare timestamps or content hashes, some rely on a change log kept by the source, and some simply re-fetch everything.
- Ingestion downloads the current version, parses it, chunks it, embeds it where the pipeline uses vectors, and writes it to the index without failing partway through.
- Retrieval behavior ensures the new chunk ranks highly for the questions it should answer, and that older chunks from the same page are removed or outranked.
Recrawling is not reindexing
The two operations are often confused. A recrawl, also called a refresh, fetches the live version of a page and indexes it. A reindex works on documents the system has already crawled and stored: it applies new parsing, chunking, or embedding settings to content you already hold. Reindexing does not pick up anything that changed on the site. If you change your chunk size and only reindex, the index will hold old pages cut into new chunks.
How stale content produces wrong answers
Microsoft’s Foundry RAG documentation on Microsoft Learn states the core constraint directly:
“If retrieval returns irrelevant or incomplete passages, the model can still produce incomplete or inaccurate answers despite grounding.”
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Staleness reaches the model through the retrieved passages in four recognizable ways.
Outdated fact retrieved
The source changed (a fee, a parameter name, a deadline), but the index still holds the old chunk. Retrieval returns it, and the answer is faithful to the passage yet wrong for today. This is the case most people mean by a stale answer, and it is the easiest to test for.
Retired content still served
A page that was deleted or superseded remains in the index. Nothing in the answer marks it as gone, and its citation may point to a page that no longer says what the answer claims, which makes the error look authoritative.
New material missing
The page that answers the question was published after the last successful crawl. Retrieval returns older passages that only partly match. The model may answer from that partial material, decline, or fill the gap from its own training knowledge. The last path is where an answer can sound confident while being unsupported by the corpus.
Mixed versions
Chunks from two revisions of the same document are retrieved together and disagree. The answer blends them, often without flagging the conflict.
Where the mechanism stops
Staleness is one route to a wrong answer, not the only one, and it is not automatic. A stale passage whose content is still true yields a correct answer. Retrieval errors unrelated to age, such as poor ranking, a parser that drops a table, or chunks dominated by navigation text, produce wrong answers from perfectly fresh data. Grounding narrows what the model is supposed to say; it does not check that the passages are true. The official documentation for the services discussed here does not quantify how much staleness raises hallucination rates, and none prescribes a universal refresh interval. Treat staleness as a risk to measure in your own evaluation set, not as a known rate.
How often to re-crawl
No vendor prescribes an interval for web content, and the documentation for the platforms below leaves scheduling to your setup. Set cadence from two inputs:
- Measured volatility. Record a content hash and last-modified value for each URL on every run, then count how often each source actually changes. A changelog and a glossary should not share a schedule.
- Cost of a stale answer. Pricing, eligibility rules, security advisories, and medical or legal guidance justify tighter refresh and monitoring than background reference material does.
Then cap the schedule by what the source and your provider will tolerate: crawl rate, site-imposed limits, robots.txt directives, and provider quotas. Frequent refreshes of a short list of high-stakes URLs often serve readers better than a full-site crawl on the same schedule.
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Refresh behavior across four platforms
The table compares what each platform’s documentation, as checked in early October 2026, describes. Service quotas and connector support change, so confirm them against the current pages before you configure a pipeline.
| Platform | Discovery and refresh | Deletions and modifications | Controls and limits | Citation metadata |
|---|---|---|---|---|
| Google Cloud Agent Search | Automatic refresh discovers new pages and recrawls existing pages on a best-effort basis; sitemap-based refresh is documented; manual recrawlUris targets literal URIs |
Not described in the documentation consulted | 20 recrawlUris calls per day per project; up to 10,000 URI values per call; a recrawl operation can run to completion or time out after 24 hours |
Not stated in the documentation consulted |
| Amazon Bedrock Knowledge Bases | Incremental sync for supported S3, Confluence, SharePoint, and Salesforce connectors; Web Crawler fetches the URLs you supply | Incremental sync is documented for the listed connectors; web page change handling is not described in the same terms | Web Crawler honors standard robots.txt directives, supports URL-pattern exclusions, and limits crawl rate | Not stated in the documentation consulted |
| Amazon Kendra Web Crawler | Full crawl sync processes new, modified, and deleted content when the data source’s change-tracking mechanism supports it | Deletions are handled through change tracking; a forced full crawl replaces indexed content on each sync | Depends on the source supporting change tracking | Not stated in the documentation consulted |
| Azure AI Search with Microsoft Foundry | No universal website recrawl schedule is established; ingestion and refresh are implementation-dependent | Implementation-dependent | Compare cost, latency, access control, and retrieval quality for your workload | Indexes can store titles, URLs, or filenames for citations |
Google Cloud Agent Search: manual recrawls are targeted
The manual path suits urgent pages, such as a corrected policy page, rather than routine coverage. With a budget of 20 calls per day per project, batch URIs into fewer calls and list each one explicitly. recrawlUris does not interpret wildcards as patterns, so an entry containing an asterisk will not expand to the pages you meant. Do not assume a recrawl operation that times out after 24 hours refreshed every URI you sent.
Amazon Bedrock Knowledge Bases: connectors versus the Web Crawler
AWS documents incremental sync for supported S3, Confluence, SharePoint, and Salesforce connectors. The Web Crawler is a separate path. It crawls the URLs you supply, honors standard robots.txt directives, applies URL-pattern exclusions, and limits crawl rate. Its per-URL status is exposed in CloudWatch, which is where skipped or failed pages show up when nothing visible has gone wrong in an answer. Do not assume a connector’s change-detection behavior carries over to web pages.
Amazon Kendra: a different service with different sync semantics
Kendra is not Bedrock Knowledge Bases, and its crawler behaves differently. Its full crawl sync can process new, modified, and deleted content, which makes it the most directly documented of these options for deletion propagation, provided the source’s change tracking supports it. Its forced full crawl is a different operation: each sync replaces the indexed content for that source. Reserve it for recovering from a corrupted index or a major site restructure rather than running it on a routine schedule.
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Microsoft’s overview describes keyword, semantic, vector, and hybrid retrieval modes, and a RAG workflow of preparation, indexing, connection, application building, and evaluation. It does not establish a universal website recrawl schedule. For a website rather than a supported data store, the crawl schedule, change detection, and deletion logic belong to your own pipeline. The index should store the title, URL, or filename fields that let answers cite their sources.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.A reference pipeline for web-scraped RAG
The steps below synthesize the vendor workflows into one architecture pattern. They are a practical design, not a requirement any single provider imposes.
- Inventory sources. Record every seed URL, sitemap, and connector with an owner and a refresh priority.
- Detect changes and deletions. Store a content hash, the last-modified value, and the HTTP status for each URL on every run. Treat a 404 or 410 as a deletion candidate, and confirm it on a second run before removing the document.
- Recrawl or sync incrementally. Use a platform’s documented incremental mode where one exists. Where it does not, re-fetch only changed and high-priority URLs, and run full recrawls on a slower schedule.
- Parse, chunk, and index the changed material. Replace all chunks of a document together, keyed by a stable document ID, so old and new chunks never coexist.
- Keep provenance. Store the source URL, fetch timestamp, content hash, and source last-modified value with every chunk, so answers can cite a page and you can tell which version was served.
- Monitor completion and failures. Track each URL as fetched, unchanged, skipped (by robots.txt or an exclusion rule), failed, or deleted. Alert on error rates and on runs that never finish.
- Test representative queries. Keep questions with expected current answers and expected source URLs. Run them after every refresh and after any parser or chunking change, and check the retrieved passages and the citation along with the final answer.
Troubleshooting freshness problems
When answers go stale, the crawl log usually looks healthy. Start from the symptom you can see.
A new page never appears in answers
- Confirm discovery: the URL appears in the sitemap or in a crawled link, and no URL-pattern exclusion matches it.
- Check its per-URL status. A robots.txt block, a rate limit, or a failed fetch is recorded there, not in the answer.
- Confirm the document exists in the index with a fetch timestamp later than the page’s publication.
- Query retrieval directly with a sentence from the page. If the chunk returns, the fault lies in generation or prompting. If it does not, inspect parsing, since navigation or script text may have displaced the content, and check the chunk boundaries.
A deleted or retired page is still cited
- Check whether the source supports deletion detection. Kendra’s full crawl sync handles deletions only when the source’s change tracking supports them; otherwise your pipeline has to detect removals.
- Compare the indexed URL list against the live sitemap and status codes, and remove documents only after a confirmed missing status on a second run.
- Look for duplicates. An archived copy, a mirror, or an old versioned URL with the same text can outrank the live page.
An updated page still produces the old answer
- Compare the page’s last-modified time with the fetch timestamp in the index. If the fetch is older than the change, the fault is in recrawl scheduling.
- If the fetch is recent but the old wording still appears, check whether old chunks survived beside new ones. Replacing by document ID prevents this.
- If you recently reindexed, recrawl the page as well, because a reindex does not fetch the live site.
- Check any answer cache in your application layer, which can serve an earlier response after the index is correct.
The crawl reports success but answers are still wrong
- Score retrieval on its own: are the expected passages in the top results for your test questions?
- Inspect parsed text for damage. Tables, prices, dates, and footnotes are often lost or reordered during extraction.
- Check whether a chunk boundary separates a fact from the condition that qualifies it.
- Only after retrieval passes should you change the prompt or the model.
Questions to settle before you choose a refresh design
Answer these for each source before you commit to a platform or schedule:
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- Discovery: how will new pages arrive? A sitemap, a seed list, link following, and a connector listing each leave different coverage gaps.
- Change detection and deletion: does the source or provider report modifications and removals, or must you infer them?
- Incremental or full refresh: can you update only what changed, and what does a full pass cost in time and load on the source?
- Scope, rate, and robots policy: which URL patterns are in and out, how fast may you fetch, and does a wider scope dilute retrieval quality?
- Access control: do authenticated or permission-restricted pages need connector support, and will the credentials remain valid for the length of a sync?
- Monitoring: how will you learn that a run finished, partly failed, or never completed?
- Provenance and citations: which fields survive into the index so that an answer can name its source?
- Retrieval mode: keyword, semantic, vector, or hybrid, and which one surfaces the wording your users actually type?
- Latency and cost: what does each refresh add to indexing time, storage, and query latency at your volume?
- Evaluation: which test questions will show whether a fresh index is also a correct one?
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