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Keeping a retrieval index fresh is an ongoing loop, not a single crawl. You need to find new, changed, and deleted pages, re-ingest what changed, and then confirm that the current versions are the ones the model actually receives. Staleness does not automatically cause hallucinations. A stale or incomplete index supplies outdated or partial evidence, and a model can then produce a wrong answer that is faithful to that evidence. Retrieval mistakes cause the same kind of failure even with a perfectly fresh index, so freshness is necessary but not sufficient.
How RAG divides the work between retrieval and generation
Retrieval-augmented generation has two stages that fail in different ways. Retrieval searches a maintained index and returns the passages most relevant to a question. Generation sends those passages, together with the question, to a language model, which writes the answer. The index exists to make that search efficient, and it usually keeps metadata such as titles or source URLs so an answer can point back to where a passage came from. Microsoft’s Foundry RAG documentation describes storing titles, URLs, or filenames in an Azure AI Search index to improve citation quality.
The two stages matter because a freshness problem and a retrieval problem look identical to the user: the answer is wrong. Working out which stage failed is most of the diagnostic work.
Why stale data produces wrong answers
A stale index can damage an answer in three ways.
- Outdated evidence. The passage was accurate when it was crawled, and the model presents it as current. If a documented API quota was raised after your last crawl, the answer states the old limit, and the citation links to a live page that now says something different. The answer was grounded, but the source it was grounded in had changed.
- Deleted content. A page that was withdrawn, retracted, or moved stays in the index. Nothing in the prompt tells the model that the passage no longer exists.
- Incomplete evidence. A partial crawl gives the model one section of a policy but not the exception that qualifies it. The answer covers the part it has, and it may fill the gap with guesses.
Microsoft’s Foundry RAG documentation states the general version of this point directly:
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“If retrieval returns irrelevant or incomplete passages, the model can still produce incomplete or inaccurate answers despite grounding.”
Grounding narrows what the model works from. It does not guarantee that the answer is correct.
Two limits keep the title’s shorthand honest. First, staleness does not necessarily cause hallucination. If a stale passage is still true, the answer built on it is correct. Second, repeating an outdated passage is a sourcing error: the model used the wrong input as instructed. Hallucination in the stricter sense, where an answer asserts something the retrieved context does not support, is one of several ways a stale or incomplete index contributes to failure. When retrieval misses the relevant passage entirely, the model may answer from its training data, which has its own cutoff and gaps. The vendor documentation reviewed in early October 2026 does not quantify how much staleness raises error rates, and it does not prescribe a universal refresh interval. Any such figure should come from measurements on your own corpus.
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Freshness depends on four layers working together. A failure at any one of them produces a stale answer that looks the same as the others.
- Source discovery. New URLs must be found through a seed list, a sitemap, a connector, or links on crawled pages.
- Change detection and recrawl or sync. The pipeline must notice that a page changed or disappeared and fetch its current version.
- Ingestion. The fetched content must be parsed, chunked, embedded if you use vectors, and written to the index without errors.
- Retrieval. Search must return the new passage and stop returning the old one.
Refreshing and reindexing are different operations
The two terms are often used interchangeably, but they do different work.
| Operation | Fetches the current source? | Use it when |
|---|---|---|
| Recrawl or sync (refresh) | Yes | The source page was added, changed, or removed |
| Reindex | No; works on documents the crawler already stored | You changed the parser, chunk size, or embedding model |
A reindex cannot pick up a change made at the source, because the pipeline never fetched the new version. Use a refresh for freshness and a reindex for changes to your own processing.
How often to re-crawl
There is no universal schedule. The major platforms describe refresh behavior, but none of them promises a freshness interval you can rely on for every source, so the interval is yours to set. Work through three steps.
- Measure change rate. Store a content hash for each URL on every crawl. After a few weeks, you will know which pages change daily, which change monthly, and which almost never change.
- Estimate the cost of a stale answer. A wrong price, eligibility rule, or security advisory is far more costly than a slightly outdated description of a stable concept.
- Set the interval from both measures. Pages that change often and matter most get the tightest schedule, or recrawls triggered by sitemap updates or CMS change events where your system can emit them. Stable reference pages can be checked weekly or monthly.
A workable starting point is two or three tiers, with frequently changing, high-stakes pages at the top and reference material at the bottom. Adjust the tiers once your change-rate data is in.
Deleted and modified content matter as much as new pages
Most RAG discussions focus on adding pages. Deletions cause confident wrong answers because the model receives no signal that a source was withdrawn. Amazon Kendra’s developer guide describes a full crawl sync mode that can process new, modified, and deleted content, provided the data source’s change-tracking mechanism supports it. Its forced full crawl mode behaves differently: it replaces the indexed content on every sync. That is thorough, but each run costs more crawl time and load, which matters for large sites.
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Where a source offers no change feed, build reconciliation yourself. Compare the URLs in the index with the URLs your crawl still sees, and remove any indexed document whose URL no longer resolves or falls outside your crawl scope. Run this on its own schedule, separate from the change crawl, so that a missed deletion does not persist indefinitely.
A practical refresh pipeline
The following sequence combines the workflows that the platforms document into one architecture pattern. It is not a requirement of any single vendor, and you can implement each step with the tools you already run.
- Register sources. Keep an inventory that records, for each source, its URL or sitemap or connector, owner, change tier, and access requirements.
- Detect changes and deletions. Use the source’s change mechanism where one exists. Where it does not, fall back to content-hash comparison and URL reconciliation.
- Recrawl or sync incrementally. Fetch only new and changed pages on the routine schedule, and reserve full crawls for cleanup and repair.
- Parse, chunk, and index changed material. Replace the old chunks for a URL in the same operation that adds the new ones. If new chunks are added without removing the old ones, both versions can be retrieved and contradict each other.
- Retain provenance. Store the source URL, fetch timestamp, content hash, and document version with every chunk, so each citation can be traced and audited.
- Monitor completion and failures. Track each run’s outcome, per-URL errors, and the age of the newest successful fetch for each tier.
- Test representative queries. After each refresh, run the questions described in the testing section below against their known current answers.
How the main platforms handle refresh
The table compares documented behavior as reviewed in early October 2026. Service behavior and quotas change, so confirm them against current documentation before you build on them.
| Platform | Documented refresh behavior | Deletions and changes | Cautions |
|---|---|---|---|
| Google Cloud Agent Search | Automatic refresh discovers new pages and recrawls existing pages on a best-effort basis. Manual recrawls target literal URIs through recrawlUris. Sitemap-based refresh is also documented. |
Not stated in the reviewed documentation. | Recrawl calls are quota-limited (see below). Wildcards are not interpreted as patterns. |
| Amazon Bedrock Knowledge Bases | Incremental sync for supported S3, Confluence, SharePoint, and Salesforce connectors. The Web Crawler fetches supplied URLs and exposes per-URL status in CloudWatch. | Described for the supported connectors; not stated for the Web Crawler in the reviewed guidance. | Connector behavior varies, including change detection, deletion, authentication, and crawl behavior. Do not assume they match. |
| Amazon Kendra Web Crawler | Full crawl sync processes new, modified, and deleted content through the data source’s change-tracking mechanism. Forced full crawl replaces indexed content on each sync. | Processed in full crawl sync when the source’s change tracking supports it. | Confirm sync-mode behavior and connector support for your deployment. Kendra and Bedrock Knowledge Bases are separate services. |
| Azure AI Search with Microsoft Foundry | Keyword, semantic, vector, and hybrid retrieval modes. The RAG workflow runs through preparation, indexing, connection, application building, and evaluation. | Not stated in Microsoft’s Foundry RAG overview. | No universal web recrawl schedule is documented there. Source refresh depends on your implementation. |
Google Cloud Agent Search recrawl limits
Google’s documented limits for manual recrawls, as reviewed in early October 2026:
- A limit of 20 recrawl calls per day per project.
- Up to 10,000 URI values in a single call.
- A recrawl operation runs until it completes or times out after 24 hours.
recrawlUristreats each value as a literal URI. Wildcards are not expanded as patterns, so a directory-wide refresh must enumerate its URIs.
Trade-offs to weigh before choosing a refresh strategy
- Crawl rate and scope. A tight schedule across a large site increases load on the source and the duration of each run. Rate limits and URL exclusions protect both sides.
- Cost and latency. Every re-embedded chunk costs compute and storage, and full crawls multiply that cost. Changing the retrieval mode between keyword, vector, and hybrid changes both latency and which passages come back.
- Source access controls. Authenticated pages, robots directives, and site terms can limit what you may fetch. Honor robots.txt as a baseline, and check the terms for each source.
- Quality and coverage. Refreshing badly parsed pages quickly carries navigation menus and cookie banners into the index. Coverage gaps leave the model with incomplete evidence.
- Document-level authorization. If users see different content, the index must enforce that per document. A fresher index that ignores permissions can expose content to people who should not see it.
Testing whether answers are current
A successful crawl job shows that fetching finished. It does not show that the model is answering from current evidence. Test the whole chain with a fixed set of questions whose current answers you know.
- Known-change check. Pick pages that changed in the last crawl and write a question whose answer changed. Confirm that the new version is retrieved and the old one is not.
- Known-deletion check. Pick a page removed from its source and confirm that it no longer appears in retrieved passages after the reconciliation run.
- Retrieval check. For each question, inspect the top retrieved passages. The passage containing the current answer should be among them.
- Citation check. Open each cited URL and confirm that it contains the claim the answer attributes to it.
- Faithfulness check. Confirm that every claim in the answer is supported by the retrieved text. Unsupported claims point to generation or prompt problems rather than freshness.
- Coverage check. For multi-part questions, confirm that each part has retrieved support, because incomplete passages produce incomplete answers.
When an answer is stale: diagnose in order
When a user reports an outdated answer, work through these stages in order and stop at the first one that fails.
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- Check the source. Fetch the URL directly. If the page still says what the answer says, the source is current and the fault lies in a later stage.
- Check the last fetch. Look at the timestamp and status for that URL. For Bedrock Knowledge Bases Web Crawler sources, check the per-URL status in CloudWatch. For Google recrawls, check the status of the recrawl operation. If the fetch failed or never ran, fix the schedule, access, or crawl scope.
- Check the index. Confirm that the stored chunks carry the current content hash and that no chunks from the old version remain.
- Check retrieval. Run the user’s exact question. If the current passage is absent from the top results, the cause is chunking, ranking, or the retrieval mode, not the crawl.
- Check generation. If the right passage was retrieved and the answer still contradicts it, the cause is in generation, such as prompt instructions or how context is ordered.
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