Choose knowledge sources for an AI support agent by asking whether each source is authoritative for the question, owned and maintained by someone accountable, current, permitted for the user, and useful for answering real support questions. There is no universally correct mix of help-center articles, product manuals, policies, APIs, and past support cases. Curate and govern the evidence first, then choose a retrieval approach that fits the questions and systems the agent must handle.
What makes a good knowledge source?
A source is useful when it can support a particular answer—not simply because it contains information. Evaluate each candidate against five questions:
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- Authority: Is this the right source of truth for this type of fact?
- Ownership: Is a person or team accountable for its accuracy and updates?
- Freshness: Can you tell when it was reviewed, when it takes effect, and whether it has been superseded?
- Permission: Is the current user and task allowed to retrieve and use it?
- Relevance: Does it answer questions customers or support agents actually ask in a form retrieval can use?
These tests apply to documents, databases, APIs, and case histories alike. AWS Prescriptive Guidance on grounding and retrieval augmented generation emphasizes that grounded responses are only as reliable as the documents, databases, or APIs behind them. Retrieval can provide evidence to a model; it cannot make inaccurate, contradictory, or incomplete source material dependable.
Compare common knowledge source types
Different sources answer different kinds of support questions. Treat this table as a starting point for assigning authority, not a rule that every agent should use every category.
| Source type | Often useful for | What to govern |
|---|---|---|
| Approved help-center articles | Customer-facing setup instructions, common questions, and standard troubleshooting | Article owner, review date, audience, publication status, and links between related or replacement articles |
| Current product manuals and specifications | Product behavior, configuration details, feature limits, and technical instructions | Product version, effective date, applicability, and conflicts with older documentation |
| Release notes and service advisories | Recent changes, known issues, and temporary service information | Whether the change is still relevant, its effective period, and how quickly updates reach the retrieval index |
| Policies and eligibility rules | Returns, warranties, account rules, and other decisions governed by policy | The accountable policy owner, applicable region or customer group, effective date, and approved wording |
| Troubleshooting runbooks and internal manuals | Agent-assist workflows and technical resolution steps that may not be suitable for customers | Intended audience, access permissions, operational owner, and whether steps are safe to expose or execute |
| Structured service APIs or systems of record | Current, account-specific facts such as a customer’s status or case information | Identity and authorization checks, field-level access, data freshness, and what the agent may disclose or do |
| Past support cases | Discovering recurring question patterns or candidate resolutions to turn into reusable knowledge | Sensitive data, duplicate cases, outdated resolutions, approval of reusable guidance, and access boundaries |
A help article may be appropriate for a general “how do I change this setting?” question, while a customer’s current plan or order status belongs in an authorized system of record. Do not treat these sources as interchangeable just because they discuss the same topic.
How to choose and govern sources
1. Start with real questions and the answer each requires
Collect representative self-service and agent-assist questions. Include product setup, troubleshooting, billing or account-specific requests, policy questions, ambiguous requests, and cases where the agent should decline or hand off. For each question, note whether the answer requires prose instructions, structured current data, a user-specific record, or a human decision.
Use actual customer wording where available, including conversational, vague, or differently phrased questions. Microsoft’s Azure guidance on retrieval augmented generation highlights that user queries may not use the same terminology as source material. A test set made only from document headings can therefore miss retrieval failures that occur in real conversations.
2. Inventory candidate sources and name their owners
For every source, record its owner, intended audience, subject area, authority, last review or effective date, update mechanism, access classification, and known limitations. Include enough identifying information—such as a stable title, URL or source ID, version, and date—to trace a retrieved passage back to its origin.
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Past cases require particular care. A resolved ticket is evidence that an agent once gave an answer, not proof that the answer is approved policy. If you use case histories, remove sensitive data, identify duplicates and outdated resolutions, and have a responsible owner approve guidance before making it reusable. AWS guidance discusses source quality and privacy risks; this review is a governance responsibility, not something to assume a connector or model performs automatically.
3. Set an authority hierarchy for each fact type
Decide which source wins for each kind of claim. A current product specification may govern product behavior; the policy owner’s current policy may govern eligibility; and an account system may govern an individual customer’s status. Record effective dates and decide what the agent should do when two sources disagree: use the designated authority, ask for clarification, or abstain and hand off.
Microsoft’s prompt-engineering guidance illustrates an explicit conflict rule by preferring official documentation over community forum posts. That is an example, not a universal hierarchy: define one that reflects your own source ownership and obligations. If no source is clearly authoritative, the agent should not resolve the conflict by choosing whichever passage sounds more convincing.
4. Define freshness, updates, and retirement
Capture publication, review, effective, and expiry dates where they exist. Set update expectations by source class: a stable conceptual article may change infrequently, whereas a release note or service advisory may become obsolete quickly. Decide how updates trigger ingestion or reindexing, and how withdrawn, expired, or superseded material is removed or demoted.
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A date field alone does not keep an answer current. The update path must reach the retrieval system, and obsolete copies must stop competing with the current source. AWS recommends versioning, freshness policies, and automated reindexing. Microsoft describes freshness-aware retrieval as a way to favor newer documents in certain configurations; confirm the current behavior of the particular service before depending on it.
5. Carry permissions and provenance into retrieval
Classify material before indexing it. Apply document-level authorization or metadata filters so the agent retrieves only content allowed for the current user and task. Preserve useful provenance—such as source title or identifier, version, date, and location—so the answer can be traced and, where appropriate, cited.
Permission checks need to apply to retrieved content, not just to the original repository. Private documents and their embeddings can create privacy and security risks if content is exposed to a user who could not access the source. Test with identities that have different permissions, including users who must not see particular documents or fields. Microsoft’s retrieval guidance calls for access controls, permission-aware indexes, filters, and source signals; AWS also highlights risks from injecting private documents into prompts.
Connector behavior is product-specific. Amazon Bedrock documentation describes document-level permission filtering for several connected source types and identifies an exception for its web crawler. That does not establish equivalent enforcement across all connectors or platforms. Check the current connector’s supported sources, sync behavior, and authorization semantics, then verify access with a test identity.
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A conventional retrieval flow can be a reasonable starting point when the corpus is bounded and question patterns are straightforward. If an answer requires the system to decide which source to query or coordinate information across multiple systems, consider a design that plans or retrieves across those sources. Microsoft’s Azure RAG guidance describes challenges such as query understanding, multiple data sources, token constraints, response-time expectations, and security governance; its agentic retrieval guidance describes query planning across sources.
Compare candidate approaches using the same support questions and operating conditions. A more elaborate retrieval design is not automatically better: it must justify its relevance, coverage, latency, cost, integration, and governance trade-offs for the task.
How to evaluate whether the source set works
Build an evaluation set from representative questions before expanding the corpus. Include paraphrases, ambiguous requests, conflicting sources, recent changes, account-specific questions, and permission-sensitive cases. For each case, identify the expected source or sources, the answer the evidence supports, and whether the correct behavior is to answer, ask a question, abstain, or hand off.
Inspect retrieval and the final response separately. A fluent answer may still rely on an outdated or unauthorized passage. Track failures in terms that point to a fix:
- Missed evidence: The appropriate source exists but was not retrieved.
- Wrong or conflicting evidence: A lower-authority or superseded source displaced the designated source of truth.
- Stale evidence: The answer or citation relies on material that should have been updated or retired.
- Unauthorized retrieval: The system exposed information that the test identity or task should not access.
- Unsupported answer: The response claims more than the retrieved evidence establishes.
- Poor failure behavior: The agent guesses where it should ask a clarifying question, abstain, or transfer the request.
- Operational friction: Updates, access rules, integrations, latency, or maintenance make the approach difficult to sustain.
Review failures with both the source owner and the retrieval operator. Depending on the cause, correct the underlying content, its metadata or permissions, the source hierarchy, or the retrieval configuration. Establish acceptance thresholds from the consequences of error and your own representative test set: the cited guidance does not establish a universal accuracy score or other numeric pass mark.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How to choose between source or platform options
When comparing sources, compare their authority for the fact, accountable owner, review and update path, access boundaries, coverage of observed questions, structure, and traceability. When comparing retrieval platforms or connectors, also compare which source types are supported and how permission filtering and synchronization work—not just whether an integration is listed.
For either decision, assess answer relevance and evidence coverage, conflict handling, freshness and stale-content removal, behavior under different user identities, citation quality, latency, retrieval cost, integration effort, and ongoing ownership. Set priorities according to support risk: an incorrect public setup instruction and an unauthorized account disclosure are different failure modes and should not be hidden inside one blended score.
No single source mix, retrieval design, or numeric quality threshold is established for all support agents. The appropriate choice depends on the questions, source landscape, permissions, and consequences of a wrong answer. Retrieval augmented generation supplies context; it does not guarantee correctness or prevent unsupported responses, so both source quality and the resulting answers need continuing evaluation.
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Frequently Asked Questions
Should a customer-facing agent use internal runbooks?
Only when the specific instructions are appropriate for the customer and the content is authorized for that audience. Keep agent-only material separate or apply audience-aware access controls; an internal resolution workflow should not automatically become customer-facing guidance.
Should an AI agent answer account-specific billing or order questions from documents?
Use an authorized, current system of record for facts about an individual account, rather than relying on a general article or an old case. Apply identity and access checks to the retrieved data and limit the response to fields the user may see.
Does retrieval augmented generation guarantee accurate answers?
No. It can provide relevant context, but an answer can still be wrong if the source is inaccurate or stale, retrieval selects the wrong evidence, or the response goes beyond what that evidence supports. The NIST discussion of retrieval augmented generation likewise does not make grounding a correctness guarantee.
Frequently Asked Questions
Should a customer-facing agent use internal runbooks?
Only when the specific instructions are appropriate for the customer and the content is authorized for that audience. Keep agent-only material separate or apply audience-aware access controls; an internal resolution workflow should not automatically become customer-facing guidance.
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Should an AI agent answer account-specific billing or order questions from documents?
Use an authorized, current system of record for facts about an individual account, rather than relying on a general article or an old case. Apply identity and access checks to the retrieved data and limit the response to fields the user may see.
Does retrieval augmented generation guarantee accurate answers?
No. It can provide relevant context, but an answer can still be wrong if the source is inaccurate or stale, retrieval selects the wrong evidence, or the response goes beyond what that evidence supports. The NIST discussion of retrieval augmented generation likewise does not make grounding a correctness guarantee.
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