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1Scan for outdated or missing drivers - takes under a minute2Repair Windows errors before they cause bigger problems3Fix the driver behind crashes, sound loss and screen glitchesChoose AI support sources by the questions they must answer, the authority of each source for those answers, who owns and updates it, and who is allowed to see it. Then set rules for conflicts, stale content, permissions, and missing evidence before connecting sources to retrieval. An AI agent can only ground its answers in the material it retrieves; retrieval does not make unreliable, outdated, or unauthorized material trustworthy.
What counts as a knowledge source for an AI support agent?
A knowledge source is material or a system that retrieval can use to help answer a customer’s question. It can be prose, structured data, or a record tied to a particular customer. These formats are not interchangeable: an article may explain how to reset a device, while an account system may be needed to establish whether a specific customer’s payment cleared.
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AWS Prescriptive Guidance describes documents, databases, and APIs as possible grounding sources and emphasizes that grounded responses are only as reliable as the underlying material. The right mix depends on the support task; no single source category is universally suitable.
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| Source class | Good fit | What to govern |
|---|---|---|
| Approved help-center articles | Customer-facing how-to instructions, common troubleshooting, and explanations of standard processes. | Article owner, review date, audience, and whether the article still matches the product or policy. |
| Product manuals and release notes | Product behavior, setup details, supported configurations, and changes tied to a particular version. | Version and effective date; distinguish current instructions from superseded guidance. |
| Policy pages | Eligibility, refunds, warranties, account terms, and other questions governed by an approved policy. | The responsible policy owner, effective date, and a conflict rule for older or informal explanations. |
| Troubleshooting runbooks | Support or agent-assist procedures that require diagnostic steps or escalation conditions. | Intended audience and permissions; internal procedures should not automatically be exposed to customers. |
| Structured service APIs or systems of record | Current, account-specific facts such as an individual customer’s status. | Authorization for the requesting user, the system’s authority for that fact, and how current values are retrieved. |
| Historical support cases | Finding recurring problems or identifying resolutions that may be converted into reusable guidance. | Sensitive data, duplicates, outdated resolutions, and owner approval before treating a case response as reusable knowledge. |
| Community discussions or informal notes | Potentially useful context for discovering customer language or issues, not automatically approved policy. | Lower or explicitly bounded authority, plus a conflict rule. Microsoft’s prompt guidance illustrates preferring official documentation over community forum posts when sources disagree. |
How to choose sources: a practical workflow
1. Start with the questions and the answer each requires
Collect representative questions from self-service and agent-assist use cases. Include setup, troubleshooting, billing or account-specific requests, policy questions, ambiguous phrasing, and requests the agent should decline or escalate. For each, identify whether a dependable answer needs instructions, a current structured value, or an authorized customer record. Microsoft’s Azure guidance notes that queries may be conversational, vague, or phrased differently from the source material, so test more than exact keyword matches.
2. Inventory candidate sources and assign accountability
For every source, record its owner, intended audience, subject area, authority for the facts it contains, last review or effective date, update mechanism, access classification, and known limitations. An inventory makes it possible to tell not only what the agent can retrieve, but who is responsible when a source is wrong or stale.
Do not treat every resolved ticket as approved guidance. If cases are used, remove sensitive information, identify duplicates and outdated resolutions, and have a responsible owner approve any reusable answer. This is a governance safeguard, not a claim that an ingestion service performs that review automatically.
3. Write an authority and conflict policy
Define the source of truth by fact type rather than declaring one repository authoritative for everything. A current product specification may govern product behavior; the policy owner’s current policy may govern eligibility; an account system may govern an individual customer’s status. Record effective dates and state what the agent should do when candidate sources disagree: prefer the designated authority, ask a clarification question, or hand off when the conflict cannot be resolved.
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4. Set freshness, update, and retirement rules
Capture publication, review, effective, and expiry dates where available. Decide how often each source class changes and what event triggers ingestion or reindexing. Define how withdrawn or superseded content is removed or demoted so an old answer does not remain a strong retrieval candidate alongside its replacement. A service advisory or release note may need a shorter review window than stable conceptual guidance.
AWS recommends versioning, freshness policies, and automated reindexing. Microsoft describes freshness-aware retrieval as an option in certain configurations. Those are implementation examples, not a guarantee that every retrieval product has the same behavior; check current service documentation before relying on a particular feature.
5. Carry access controls and provenance into retrieval
Classify content before indexing it. Apply document-level authorization or metadata filters so a result is eligible for the current user and task, not merely present in the index. Preserve useful provenance such as the source title or identifier and its version or date. That helps users understand an answer’s basis and lets operators investigate a questionable response.
Microsoft’s retrieval guidance calls out verified sources, access controls, trust and freshness signals, permission-aware indexes, filters, and citations. AWS warns that private documents introduced into prompts create privacy risks. Connector behavior is product-specific: Amazon Bedrock documentation describes document-level permission filtering for several connected source types and an exception for its web crawler. That example does not establish equivalent enforcement across all connectors or platforms. Check the current connector’s coverage, synchronization behavior, and authorization semantics using a test identity.
6. Fit retrieval design to the corpus and question complexity
A conventional retrieval flow may be a sensible starting point when the corpus is bounded and questions are straightforward. If a question requires selecting among sources or coordinating information across systems, consider whether query planning or multi-source retrieval is needed. Microsoft’s Azure guidance identifies query understanding, multi-source access, token limits, response-time expectations, and security and governance as RAG challenges; its agentic retrieval guidance describes planning across queries and sources.
Compare an implementation on evidence and operating demands, not on the number of connected repositories. Consider relevance and evidence coverage, authority and conflict handling, update effort and stale-content removal, permissions across user identities, citation traceability, latency and retrieval cost under expected traffic, integration and ownership, and what happens when evidence is missing. The official guidance cited here does not establish universal numeric thresholds for these measures; set acceptance criteria according to the risk of the support use case.
7. Pilot against realistic cases, then maintain
Build an evaluation set from real question patterns. Include paraphrases, ambiguous requests, conflicting sources, recently changed information, and permission-sensitive cases. Inspect what was retrieved as well as the final answer: fluent wording can still be based on the wrong document.
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- Review failures with both the source owner and the retrieval operator; correct the content, metadata, permissions, or retrieval configuration responsible.
- Repeat checks when sources, access rules, connectors, or retrieval behavior change.
AWS and Microsoft guidance supports attention to source quality, freshness, relevance, and access controls, but does not prescribe a universal evaluation protocol or pass score. RAG supplies context; it does not guarantee correctness or prevent unsupported answers.
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How to compare candidate sources or retrieval platforms
For source classes, compare their authority for the fact in question, accountable owner, update path, access boundaries, structure, coverage of observed questions, traceability, and maintenance effort. For platforms, also compare the exact connectors and permission-filtering behavior available for your source systems. A broad integration claim alone does not establish that permissions or updates behave as needed.
Use a small set of representative questions to test whether each candidate source contributes current, authorized evidence. Include cases where two sources conflict, a source has changed, or the user should not see the result. There is no universal best source mix or quality score established by the official guidance discussed here; the evaluation needs to reflect your content, user permissions, and support risks.
Frequently Asked Questions
Frequently Asked Questions
Should an AI support agent use support tickets as a knowledge source?
They can help identify recurring issues, but a ticket response is not automatically approved policy. Before reusing case material, remove sensitive information, check for duplicates and outdated resolutions, and have an accountable owner approve the guidance.
How can I stop an AI agent from retrieving information a user cannot access?
Apply document-level authorization or metadata filters during retrieval, preserve source permissions in the connected index, and test with identities that have different access rights. Verify the behavior of each connector; permission enforcement is not established merely by connecting a source.
Does retrieval-augmented generation guarantee accurate support answers?
No. Retrieval can supply relevant context, but answer quality still depends on the authority, accuracy, and freshness of the source material and on retrieval and answer-generation behavior. Evaluate retrieved evidence and final responses together.
Is a help center enough for an AI customer service agent?
It may cover public instructions and standard questions, but account-specific facts or internal support procedures can require different sources with separate access boundaries. Choose sources based on the evidence each question needs.
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