B2B SaaS 的 Vertex AI 连接备忘录: 区分销售 FAQ 与支持历史

在 B2B SaaS 中，销售 FAQ、帮助中心、支持工单、商谈记录、路线图和 Slack 回答都会像 AI 的好材料。若客户名、合同条件、故障记录和未公开功能进入同一索引，FAQ 就会变成泄漏路径。

本文用 Claude Code 在实现前起草 Vertex AI 连接备忘录。目标是分开销售 FAQ 和支持历史，减少商谈前不安，同时避免把客户数据粗暴交给 AI。

本文要点

• Keep sales FAQ, help content, support history, deal notes, and roadmap data in separate buckets.

• Use Vertex AI RAG, Grounding, Agent Search, and safety filters after data boundaries are clear.

• Claude Code drafts classification tables, test questions, prompts, and operations notes.

• Humans decide customer names, contracts, incidents, unpublished features, legal wording, and pricing answers.

• Send implementation-heavy readers to training because every SaaS has different data rules.

SaaS AI FAQ 项目常见失败

先分四个桶：公开销售 FAQ、支持 FAQ、内部确认、不要交给 AI。Vertex AI RAG、Grounding、Agent Search 数据存储和安全过滤可以让回答更有依据，但不能替你判断哪些 SaaS 数据适合公开或售前回答。

Official references: RAG Engine, Grounding overview, Grounding with Google Search, Agent Search data stores, Safety filters, and Responsible AI.

流程：Vertex AI 前先分数据

先分四个桶：公开销售 FAQ、支持 FAQ、内部确认、不要交给 AI。Vertex AI RAG、Grounding、Agent Search 数据存储和安全过滤可以让回答更有依据，但不能替你判断哪些 SaaS 数据适合公开或售前回答。

| Source | AI use | Human review |

| --- | --- | --- |

| Sales FAQ | public features, public pricing, buying questions | legal wording and pricing examples |

| Help center | operations, settings, roles, errors | old UI and deprecated features |

| Support history | anonymized patterns and frequent errors | customer names, contracts, incidents |

| Deal notes | usually no direct AI use | budget, discount, roadmap, buyer name |

Claude Code 负责什么，人负责什么

先分四个桶：公开销售 FAQ、支持 FAQ、内部确认、不要交给 AI。Vertex AI RAG、Grounding、Agent Search 数据存储和安全过滤可以让回答更有依据，但不能替你判断哪些 SaaS 数据适合公开或售前回答。

3 个 Use case

Use case 1: Split sales FAQ and support history

• Input: public FAQ, help articles, support CSV columns, ticket tags, pre-sales questions.

• Output: four-bucket table: sales FAQ, support FAQ, internal review, do-not-send-to-AI.

• Human review: check customer names, contracts, incident history, unpublished features, and special pricing.

Use case 2: Build test questions before Vertex AI

• Input: buyer persona, pricing page, help articles, prohibited answers, escalation rules.

• Output: allowed questions, handoff questions, and questions requiring source links.

• Human review: decide boundaries for pricing, contracts, security, incidents, cancellation, and custom requests.

Use case 3: Create operations notes for wrong answers

• Input: wrong answers, ticket logs, support corrections, FAQ update history.

• Output: issue class, data source to fix, retest question, and approver.

• Human review: legal wording, refund terms, incident explanations, customer impact, and release timing.

可复制提示词

Create a Vertex AI integration note for a B2B SaaS. Goal: separate sales FAQ from support history and avoid sending customer data into broad AI context. Create four buckets, twenty test questions, handoff rules, source-link rules, and operations notes. Do not paste real customer values. Main CTA: /training/.

可运行检查代码

const faqCandidates = [{ source: 'support_history', title: 'Client incident response', bucket: 'sales_faq', fields: ['customerName', 'incidentId', 'contractNote'] }];

const sensitiveHints = [/customer/i, /contract/i, /incident/i, /discount/i, /account/i];

const findings = faqCandidates.flatMap((item) => {

  const sensitiveFields = item.fields.filter((field) => sensitiveHints.some((pattern) => pattern.test(field)));

  return item.bucket === 'sales_faq' && sensitiveFields.length ? [{ title: item.title, issue: sensitiveFields.join(', ') }] : [];

});

console.table(findings);

if (findings.length) process.exitCode = 1;

Pitfall: RAG 会让错误数据听起来正确

先分四个桶：公开销售 FAQ、支持 FAQ、内部确认、不要交给 AI。Vertex AI RAG、Grounding、Agent Search 数据存储和安全过滤可以让回答更有依据，但不能替你判断哪些 SaaS 数据适合公开或售前回答。

Cause: RAG brings answers closer to the indexed data. If the indexed data is wrong for sales use, the answer becomes confidently wrong or unsafe. Fix: split data before ingestion, use anonymized support patterns, and keep contracts, incidents, and roadmap notes out of public FAQ context.

FAQ

Q. What should come first? A. A data classification table, not a model setting.

Q. Should all support history be removed? A. No. Patterns and tags can help, but customer-specific details need separation.

Q. Why training? A. Vertex AI, Agent Search, RAG, data boundaries, and security review differ by SaaS.

咨询入口

ClaudeCodeLab training can help create FAQ buckets, test questions, and operations notes before Vertex AI or Agent Search implementation.

实际检查结果

This article checks frontmatter, official links, three use cases, input-output-human review labels, code fences, and the /training/ CTA. The JavaScript sample detects customer, contract, and incident fields mixed into sales FAQ candidates.