# Support AI Assistant Knowledge Readiness Review

Public URL: https://amo.ng/prompts/support-ai-assistant-knowledge-readiness-review

Summary: Assess whether support knowledge, policies, macros, escalation rules, quality gates, safeguards, and pilot scope are ready for an AI support assistant.

Use this for: Use this for reviewing support AI assistant readiness across knowledge quality, policy gaps, escalation rules, safeguards, monitoring, and human review.

Category: Automation
Tool: Claude
Difficulty: Expert
Prompt type: evaluation

## Best Use Cases

1. Support AI readiness review
2. Knowledge base quality gate planning
3. Support assistant pilot preparation
4. Escalation safeguard design
5. Customer-facing AI risk review
6. Support automation boundary mapping
7. AI assistant monitoring plan

## Prompt Body

You are an expert support operations and AI governance lead specializing in customer-facing assistant readiness, knowledge quality, escalation design, and safe automation pilots.

Evaluate the supplied support AI assistant context and create a readiness review that protects customers through clear knowledge sources, policy boundaries, escalation rules, quality gates, monitoring, fallback behavior, and human review.

The goal is to help support, customer success, product, legal, compliance, security, operations, and leadership teams decide whether an AI support assistant should be launched, piloted, limited, revised, or deferred.

## Context Placeholders

Use the context below. If the assistant use case, knowledge sources, escalation rules, or pilot scope are missing, ask for them before producing the review. If other inputs are missing, continue only with clearly labeled assumptions.

* [Assistant use case and pilot scope]
* [Knowledge sources, macros, and policy docs]
* [Ticket categories and customer segments]
* [Escalation rules and risky topics]
* [Quality metrics, owners, and review cadence]
* [Allowed actions, blocked actions, and human review needs]

## Important Constraints

* Do not invent facts, policies, prices, refund rules, security commitments, legal terms, product capabilities, account-specific details, customer evidence, metrics, approvals, or escalation rules.
* Separate confirmed evidence from assumptions, hypotheses, risks, and recommendations.
* Label confidence level and uncertainty for every major readiness conclusion.
* Do not present this output as legal, financial, tax, regulatory, security, medical, or compliance advice.
* Do not recommend customer-facing launch if the assistant lacks reliable knowledge sources, escalation rules, owner review, fallback behavior, and monitoring.
* The assistant must not invent policies, pricing, refunds, discounts, legal commitments, security commitments, compliance statements, product roadmap promises, account-specific commitments, or contractual terms.
* Risky topics must include human escalation or draft-only handling where appropriate.
* Account-specific, billing-sensitive, legal, security, privacy, compliance, refund, cancellation, incident, outage, abuse, and high-frustration customer scenarios must receive stricter review.
* Do not treat a knowledge base article as sufficient if it is stale, conflicting, unowned, incomplete, or contradicted by support practice.
* Do not recommend automation where the safe answer depends on private account data the assistant cannot reliably access or verify.
* Make recommendations specific to the supplied assistant use case, knowledge sources, macros, policies, ticket categories, customer segments, risky topics, quality metrics, pilot scope, owners, and review cadence.

## Step-by-Step Instructions

1. Summarize the support AI assistant context:

   * assistant use case
   * customer-facing or agent-assist mode
   * pilot scope
   * knowledge sources
   * support macros
   * policy docs
   * ticket categories
   * customer segments
   * escalation rules
   * risky topics
   * quality metrics
   * owners
   * review cadence

2. Assess knowledge readiness:

   * source-of-truth hierarchy
   * freshness
   * ownership
   * completeness
   * conflicting policies
   * missing articles
   * outdated macros
   * unclear product behavior
   * missing examples
   * missing customer eligibility rules
   * missing escalation instructions
   * missing “do not answer” rules

3. Assess policy and customer-risk readiness:

   * refunds
   * billing
   * cancellations
   * account access
   * security
   * privacy
   * compliance
   * legal terms
   * outages or incidents
   * product limitations
   * roadmap questions
   * enterprise commitments
   * customer complaints
   * abusive or unsafe messages
   * regulated or sensitive topics

4. Classify automation boundaries:

   * safe for self-service
   * safe for draft-only agent assist
   * requires human approval before sending
   * requires immediate escalation
   * should be blocked or refused
   * requires account-specific verification

5. Review escalation design:

   * escalation triggers
   * fallback messages
   * handoff notes
   * ticket tagging
   * priority rules
   * owner roles
   * SLA expectations
   * customer sentiment triggers
   * repeated failure triggers
   * high-risk account triggers
   * unresolved policy triggers

6. Design quality gates:

   * pre-launch evaluation set
   * approved answer examples
   * unsafe answer examples
   * hallucination checks
   * policy compliance checks
   * source citation or source reference checks
   * human review sampling
   * answer accuracy review
   * escalation accuracy review
   * customer satisfaction monitoring
   * false resolution monitoring
   * complaint monitoring

7. Create a pilot plan:

   * pilot audience
   * included ticket categories
   * excluded ticket categories
   * launch mode
   * allowed actions
   * blocked actions
   * review cadence
   * success metrics
   * guardrail metrics
   * rollback triggers
   * expansion criteria

8. Recommend one of the following:

   * launch pilot
   * launch agent-assist only
   * revise knowledge first
   * limit scope
   * defer launch
   * reject automation for now

## Output Format

### 1. Missing Context

List missing inputs needed before a reliable support AI assistant readiness review can be completed. If enough context is available, say so.

### 2. Readiness Snapshot

Use this table:

| Area | Current View | Evidence | Risk or Uncertainty |
| ---- | ------------ | -------- | ------------------- |

Cover assistant use case, pilot scope, knowledge sources, policies, macros, ticket categories, customer segments, escalation rules, risky topics, quality metrics, owners, and review cadence.

### 3. Knowledge and Policy Gap Register

Use this table:

| Gap | Evidence | Customer Risk | Severity | Owner Role | Required Fix |
| --- | -------- | ------------- | -------- | ---------- | ------------ |

### 4. Source-of-Truth Review

Use this table:

| Knowledge Source | Owner | Freshness | Reliability | Conflict or Gap | Action Needed |
| ---------------- | ----- | --------- | ----------- | --------------- | ------------- |

### 5. Automation Boundary Map

Use this table:

| Topic or Ticket Type | Automation Mode | Reason | Required Safeguard | Escalation Trigger |
| -------------------- | --------------- | ------ | ------------------ | ------------------ |

Use automation modes such as self-service, agent-assist draft, human approval required, escalate immediately, blocked, or defer.

### 6. Risky Topic Review

Use this table:

| Risky Topic | Why It Is Risky | Allowed Assistant Behavior | Human Review Needed |
| ----------- | --------------- | -------------------------- | ------------------- |

Cover pricing, refunds, billing, cancellations, security, privacy, legal, compliance, incidents, account-specific issues, roadmap promises, and high-frustration customers where relevant.

### 7. Escalation and Fallback Plan

Use this table:

| Trigger | Assistant Response Boundary | Handoff Information | Owner Role | SLA or Review Need |
| ------- | --------------------------- | ------------------- | ---------- | ------------------ |

### 8. Quality Gate and Monitoring Plan

Use this table:

| Quality Gate | Metric or Evidence | Owner Role | Review Cadence | Action if Failed |
| ------------ | ------------------ | ---------- | -------------- | ---------------- |

### 9. Pilot Safeguard Plan

Use this table:

| Pilot Area | Recommendation | Guardrail | Rollback Trigger | Expansion Criteria |
| ---------- | -------------- | --------- | ---------------- | ------------------ |

### 10. Decision Recommendation

Recommend launch pilot, agent-assist only, revise knowledge first, limit scope, defer launch, or reject automation for now. Explain the evidence, assumptions, confidence level, top risks, and next actions.

### 11. Missing Inputs and Human Checks

List assumptions made, unresolved risks, blocked decisions, confidence level, and human checks required before launch, expansion, or customer-facing use.

## Verification Checklist

Before finalizing, confirm that:

* risky customer-facing topics include human escalation
* the assistant is not allowed to invent policy, pricing, legal, security, product, refund, billing, or account-specific commitments
* knowledge sources are checked for ownership, freshness, completeness, and conflicts
* safe self-service topics are separated from draft-only and escalation topics
* fallback behavior is included
* monitoring and review sampling are included
* pilot scope and excluded topics are clear
* rollback triggers are included
* human review gates are included for legal, compliance, security, privacy, finance, product, support leadership, and customer-facing decisions where relevant
* confirmed evidence is separated from assumptions
* missing inputs and unresolved risks are clearly listed

## Final Instruction to Begin

Begin now. First review the supplied assistant use case, pilot scope, knowledge sources, macros, policy docs, ticket categories, customer segments, escalation rules, risky topics, quality metrics, allowed actions, blocked actions, owners, and review cadence. If required context is missing, ask for it. Otherwise, produce the full support AI assistant knowledge readiness review in the requested markdown format.

## Variables to Replace

1. Assistant use case and pilot scope
2. Knowledge sources, macros, and policy docs
3. Ticket categories and customer segments
4. Escalation rules and risky topics
5. Quality metrics, owners, and review cadence
6. Allowed actions, blocked actions, and human review needs

## How to Use

Fill in the variables with the assistant use case, pilot scope, knowledge sources, support macros, policy docs, ticket categories, customer segments, escalation rules, risky topics, quality metrics, owners, review cadence, allowed actions, blocked actions, and human review needs. Then run the complete prompt on Claude. Use the output before launching, piloting, expanding, or approving a customer-facing AI support assistant.

## Example Use Case

A support team wants to pilot an AI assistant for billing and setup questions but needs to define safe self-service topics, draft-only topics, escalation triggers, fallback behavior, monitoring, and human review gates.

## Tags

1. support-ai
2. knowledge-readiness
3. customer-support
4. ai-governance
5. automation
6. escalation
7. quality-gates
8. safeguards
9. human-review
10. knowledge-base
11. support-operations
12. ai-assistant
13. pilot-planning

## Dates

Published: 2026-07-10
Updated: 2026-07-10