Companies keep enterprise AI answers current by retrieving information from authoritative internal sources when a question is asked—or by keeping a searchable index synchronized with those sources. For indexed systems, that means reliably processing additions, edits, and deletions, measuring how long changes take to become searchable, and enforcing each user’s access rights at retrieval time. Retrieval-augmented generation (RAG) can ground answers in current company information, but it does not guarantee that the answer is correct.
Use retrieval for changing facts, not model retraining
RAG retrieves relevant material from an index or data store, supplies it alongside the user’s question as grounding context, and asks the model to generate a response. The model can then use organization-specific information instead of relying only on what it learned during training. Microsoft describes the choice succinctly: “Use RAG when you need answers grounded in private or frequently changing data.” Microsoft Foundry documentation
Fine-tuning serves a different purpose: it can change a model’s behavior, style, or task performance, but it is not a practical substitute for a pipeline that refreshes changing company facts. If data changes often or access is restricted, design the retrieval and synchronization path around those requirements. An index may support keyword, semantic, vector, or hybrid retrieval; metadata such as document titles and URLs can help preserve traceability. Microsoft’s RAG guidance
Choose a live query or a synchronized index
The central architecture decision is whether to ask an authoritative system for information when a question arrives or search a copy that is kept up to date. Connector availability, authentication, data formats, and synchronization behavior vary by platform, so validate the particular source and deployment before promising a freshness level.
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| Approach | How it gets current information | Key operational consideration |
|---|---|---|
| Live connector or API query | Queries a supported source at answer time; Microsoft Copilot Studio documents real-time connectors for structured data in systems including Salesforce, ServiceNow, Zendesk, and Azure SQL. | Check source coverage, connector behavior, authentication, and whether the query enforces the user’s permissions. A live path may avoid a separate indexing cycle, but its freshness and availability depend on the specific connector and source. Microsoft Copilot Studio documentation |
| Scheduled index synchronization | Copies and indexes source content on a configured schedule, then retrieves relevant passages for each question. | Freshness depends on the schedule, successful processing, and propagation into the searchable store. AWS announced daily, weekly, or monthly automatic sync scheduling for native Amazon Bedrock Managed Knowledge Base data source connectors on September 4, 2026; those options are not a universal freshness guarantee. AWS announcement |
| Change-triggered indexing with scheduled reconciliation | Starts updates when source changes are detected, with periodic syncs used to catch missed changes where the connector supports this pattern. | Confirm that the chosen source and connector support the required triggers and that failures can be detected and recovered. The available sources do not establish a universal event-driven mechanism or service guarantee across platforms. |
Use a periodic index for material that changes occasionally and can tolerate its measured lag. For volatile records or answers where stale information could cause material harm, assess a faster update path or a direct query. In either case, set the target freshness from the consequences of delay—not from a vendor’s sync setting alone.
Make synchronization handle the full change lifecycle
An index is only as current as the process that maintains it. For every connected source, define how the system detects and applies new records, edits, metadata changes, and deletions. Deletions matter: a removed or access-revoked document that remains retrievable can produce a misleading answer or expose information that should no longer be available.
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Amazon Bedrock’s documented knowledge-base sync is incremental: new documents are ingested; changed content or metadata is re-ingested; deleted documents are removed; and unchanged documents are skipped. Re-ingestion includes parsing, chunking, embedding generation, and indexing. AWS sync documentation
Do not treat “sync completed” as equivalent to “every change is queryable.” AWS notes that new vector embeddings can take a few minutes to appear when the vector store is not Amazon Aurora. That is a platform-specific example of propagation lag, not a guarantee for other connectors or stores. AWS sync documentation
Synchronization can also be asynchronous at other stages. Google says a source change or periodic synchronization can start a batch update to Gemini Enterprise Private Knowledge Graph; the graph remains active while that update runs and may be out of sync. Google also says regenerated query annotations can return after up to a day when the private graph is enabled. An active service status therefore does not by itself establish that derived data is current. Google Cloud documentation
Enforce permissions when retrieving, not just when indexing
Fresh data is not safe data unless the assistant returns only information the current user may access. Apply authorization at retrieval time and verify how each connector represents identity. A broad service account or shared index can otherwise make a correctly synchronized document visible to users who should not see it.
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Microsoft documents that SharePoint and OneDrive results in Copilot Studio use delegated Microsoft Entra ID authentication and security trimming, returning only content the user can read. It also notes that Azure AI Search connections that do not use delegated user authentication do not provide that trimming by themselves. The permission model depends on the specific connection; test it with users who have different access levels. Microsoft Copilot Studio documentation
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Evaluate answers as well as freshness
RAG reduces dependence on a model’s stored knowledge by providing retrieved context, but the result can still be wrong. Irrelevant or incomplete passages can yield incomplete or inaccurate answers; poor preparation, chunking, indexing, or prompt design can also hurt quality. Retrieved documents should be treated as untrusted input because they may contain prompt-injection attempts. Microsoft recommends testing retrieval and answer quality and using citations. Microsoft’s RAG guidance
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Build evaluation around representative questions and known source records. Check whether the right passage was retrieved, whether the response is supported by that passage, whether the citation points to a useful source, and whether the answer changes appropriately after a record is edited or deleted. Include permission tests and adversarial documents, not just ordinary search queries.
Track operational measures that reveal distinct failure modes:
- Freshness lag: elapsed time from a source change to that change becoming retrievable.
- Synchronization health: failed, incomplete, or delayed jobs, plus whether additions, edits, metadata changes, and deletions are reflected.
- Retrieval and answer quality: relevance and coverage of retrieved material, factual correctness, and citation quality.
- Access control: whether unauthorized users can retrieve restricted content.
- Performance and cost: retrieval latency, connector overhead, embedding work, and model context consumed by retrieved passages.
Retrieval introduces extra compute and round trips; embeddings have indexing and often query-time costs, and retrieved passages use input tokens. The trade-off is operational as well as architectural. Microsoft’s RAG guidance
Quick Recap
Set a freshness objective and test the complete path
- Classify the information. Identify which records change, how often they change, how harmful a stale answer would be, and which users may see them.
- Select the access path. Use a live query when supported and appropriate, or an index with a sync cadence and permissions model that meet the need.
- Define freshness in measurable terms. Set a maximum acceptable source-to-retrieval delay for the use case; do not infer it from a schedule label or a healthy service status.
- Test additions, edits, and deletions. Make controlled changes in the source and measure when answers begin reflecting them—or stop returning removed information.
- Exercise failure and access cases. Confirm that failed syncs surface warnings, stale or missing sources are handled appropriately, and users cannot retrieve documents outside their permissions.
- Monitor and refine. Review lag, job health, answer quality, security, latency, and cost, then adjust the sync pattern, retrieval configuration, or source path as conditions change.
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