Knowledge Quality and Release Checklist

Before launching an AI support knowledge base, confirm that the knowledge can be used reliably in real support workflows.

1. Product Facts

• Product name, specifications, sizing, materials, and usage scope are clear.
• Outdated products, old campaigns, and conflicting descriptions are removed.
• Store, country, or language differences are marked.
• Common customer misunderstandings are documented separately.
• Claims that should not be promised are clearly forbidden.

2. Logistics and Fulfillment

• Shipping timelines are separated by channel, region, and campaign period.
• Logistics exceptions have standard explanations and escalation rules.
• Cross-border clearance, remote regions, and holiday delays have separate wording.
• Cases that require order or logistics verification are marked.
• Definite commitments are forbidden before verification.

3. After-Sales Policies

• Return, exchange, refund, reshipment, and compensation rules are separated.
• Each policy defines applicability, exceptions, and evidence requirements.
• Platform rules and brand-owned policies are distinguished.
• High-risk commitments are marked for human review.
• Disputes, complaints, and negative reviews have escalation rules.

4. Brand Tone

• Address, tone, length, and reassurance style are defined.
• Public comments, private messages, marketplace channels, and private-domain channels have separate wording rules.
• Banned terms, over-promises, and sensitive expressions are listed.
• Multilingual or regional versions are prepared when needed.
• Human edits can become pending knowledge updates.

5. Risk Boundaries

• Which issues can be auto-replied.
• Which issues can only produce drafts.
• Which issues require review before send.
• Which back-office actions are read-only.
• Whether abnormal login, permission, or page states pause automation and escalate.

6. Test Samples

Prepare at least three sample groups before launch:

• high-frequency questions to validate coverage and speed;
• boundary questions to validate refusal, human takeover, and review triggers;
• historical complaints to validate risk detection and evidence requirements.

Each test sample should include expected answer, applicable knowledge, risk level, and review requirement.

7. Release Rhythm

Use small releases:

1. publish first in draft mode;
2. watch agent acceptance and edits;
3. resolve knowledge conflicts;
4. enable automation for low-risk, high-frequency issues;
5. review unresolved issues, complaint samples, and review rejections weekly.

Acceptance Criteria

The knowledge base is ready for wider use when:

• high-frequency question coverage is stable;
• cited knowledge is explainable;
• refunds, compensation, complaints, and other high-risk issues trigger review;
• human edits can flow back into knowledge;
• managers can see version, test, and release records.

Knowledge quality is not about volume. It is about being usable, controlled, and iterative.