中芸汇科技
2026-05-30
Project AcceptanceAI StandardsQuality Management
Article illustration
Article illustration

AI project acceptance adds an 'effectiveness' dimension beyond traditional software—100% functional pass is not enough; core scenario accuracy must be ≥95%, hallucination rate ≤3%, P99 latency ≤5s, and security must pass 100%. According to the China Academy of Information and Communications Technology's '2025 AI Application Development Report', the lack of systematic acceptance criteria is the primary cause of AI project disputes. This article offers a complete four-dimensional acceptance criteria template: functionality, performance, security, and effectiveness, making acceptance evidence-based.

How to Pass Functional Acceptance?

Basic Functional Acceptance

Acceptance ItemAcceptance CriterionTest Method
All features implemented100% of contracted features implementedVerify item by item against the functional test checklist
Authorization controls effectiveDifferent roles see different contentMulti-role testing
Data flow normalData correctly synchronized across systemsEnd-to-end process testing
Exception handling correctPrompts and fallback mechanisms for exceptionsException scenario testing

AI-Specific Functional Acceptance

Acceptance ItemAcceptance CriterionTest Method
Intent recognitionCore intent recognition accuracy ≥90%Verify with 200+ test cases
Knowledge retrievalRecall@10 ≥85%Evaluate on standard test set
Answer generationAnswer accuracy ≥85%Human annotation of 100+ real questions
Human handoffSeamless handoff process with complete contextSimulate low-confidence scenarios

What Are the Performance Acceptance Criteria?

MetricStandard ValueTest Condition
Average response time≤2sNormal load
P99 response time≤5sNormal load
Peak throughput≥ contracted valueStress test
System availability≥99.9%7-day continuous run
GPU memory usage≤ contracted valueSustained running
Concurrency support≥ contracted concurrencyConcurrency test

What Are the Mandatory Security Acceptance Checks?

Data Security

Acceptance ItemStandardTest Method
Data transmission encryptionTLS 1.2+Verify via packet capture
Data storage encryptionAES-256Configuration check
Sensitive data maskingID numbers/phone numbers/bank card numbers100+ test cases
Access controlRBAC + document-level permissionsUnauthorized access testing

AI Security

Acceptance ItemStandardTest Method
Prompt injection defenseMalicious commands are not executed50+ injection attack tests
Hallucination controlCore scenario hallucination rate ≤5%Human annotation verification
Output filteringNon-compliant content is not outputSensitive word + non-compliant content testing
Operation auditAll critical operations are loggedLog integrity check

Security Test Checklist

  • [ ] Penetration testing: No high-risk vulnerabilities
  • [ ] Unauthorized access test: All cross-role accesses are blocked
  • [ ] Injection test: All prompt injection attacks are defended
  • [ ] Data leakage test: Sensitive data does not leave the system
  • How to Evaluate Effectiveness Acceptance? This Is a Dimension Unique to AI Projects

    Effectiveness Indicators

    ScenarioAccuracy TargetHallucination Rate Target
    Core scenarios≥95%≤3%
    General scenarios≥85%≤10%
    Edge scenarios"I don't know" allowed

    Effectiveness Test Methods

    MethodSample SizePerformed By
    Automated evaluation500+ itemsTechnical team
    Human-annotated evaluation100+ itemsBusiness team
    Real user testing50+ usersTarget users
    A/B comparisonCompare with old systemOperations team

    Effectiveness Decay Testing

    Run continuously for 7 days; accuracy fluctuations must not exceed ±3%.

    What Does Documentation Acceptance Include?

    Document TypeMust Include
    Operation manualUser operation steps, screenshots, FAQs
    O&M manualSystem architecture, deployment steps, monitoring metrics, emergency plans
    API documentationInterface descriptions, request/response examples, error codes
    Training materialsTraining PPT, video tutorials, assessment questions
    Knowledge base managementDocument update process, templates, quality standards

    How Is the Acceptance Process Conducted?

    ```

    Pre-acceptance (Internal) → Fix Issues → Formal Acceptance (Client Participation)

    Functional Acceptance → Performance Acceptance → Security Acceptance → Effectiveness Acceptance → Documentation Acceptance

    Acceptance Report → Outstanding Issues List → Rectification within Deadline → Go-Live

    ```

    Acceptance Pass Criteria

  • Functional acceptance: 100% passed
  • Performance acceptance: 100% passed
  • Security acceptance: 100% passed
  • Effectiveness acceptance: Core scenarios 100% passed, general scenarios ≥90% passed
  • Documentation acceptance: 100% passed
  • No P0-level outstanding issues
  • Frequently Asked Questions

    What Is the Difference Between 'Effectiveness Acceptance' in AI Projects and 'Functional Testing' in Traditional Software?

    Functional testing in traditional software is binary—a feature either exists or not, and outcomes are predictable. Effectiveness acceptance in AI projects is probabilistic—the same input may produce different outputs, with accuracy being 95% rather than 100%. Therefore, AI projects must agree on 'accuracy thresholds' and 'test sample sizes,' rather than simply checking whether a feature is implemented.

    How Are 'Core Scenarios' and 'General Scenarios' Defined in Effectiveness Acceptance?

    Core scenarios are those that directly impact business revenue or compliance (e.g., risk control approval, compliance queries), with an accuracy target of ≥95%. General scenarios are supplementary (e.g., product recommendations, usage guidance), where ≥85% accuracy is sufficient. It is recommended to agree in writing on scenario classification and corresponding standards with the business side at project initiation.

    What Should Be Done If Effectiveness Declines After AI Project Acceptance?

    During acceptance, an 'effectiveness warranty period' should be specified—usually 3–6 months. Within this period, if accuracy drops by more than 5%, the vendor should optimize for free. Common causes of decline include outdated knowledge bases, changing business rules, and data distribution drift. The acceptance documentation should include periodic optimization mechanisms and responsibility assignment.

    Looking to establish AI project acceptance criteria? Book a Free Acceptance Consultation