Why you need this
AI systems are non-deterministic—the same prompt can produce different outputs. Without systematic testing, you can't guarantee quality, safety, or reliability. Many teams ship AI features that work 80% of the time, only to face customer complaints about the other 20%.
The problem: Traditional software testing approaches don't work for AI. You can't just write unit tests and call it done. AI outputs need evaluation across multiple dimensions: accuracy, safety, bias, consistency, and edge case handling.
This framework solves that. It provides structured methodologies for testing AI systems at every stage—from initial development through production monitoring.
Perfect for:
- QA engineers testing AI-powered features
- Product managers validating AI output quality
- ML engineers evaluating model performance
- DevOps teams implementing AI monitoring and observability
What's inside
Comprehensive Testing Methodology
Functional Testing:
- Output accuracy verification
- Intent recognition validation
- Response completeness checks
- Edge case scenario testing
- Hallucination detection methods
Quality Assessment:
- Relevance scoring frameworks
- Coherence and consistency evaluation
- Tone and style verification
- Formatting and structure validation
- Citation and fact-checking protocols
Safety & Ethics Testing:
- Bias detection across demographic groups
- Harmful content filtering validation
- Privacy and data leakage prevention
- Guardrail effectiveness testing
- Compliance verification (GDPR, industry regulations)
Performance Testing:
- Latency and response time benchmarks
- Token consumption tracking
- Cost per request analysis
- Throughput under load
- Failure rate monitoring
Regression Testing:
- Prompt change impact analysis
- Model version comparison
- Output drift detection
- Historical performance baselines
Each Testing Category Includes:
- ✓ Test case templates
- ✓ Success criteria definitions
- ✓ Sample test data sets
- ✓ Scoring rubrics and metrics
- ✓ Automated testing tool recommendations
How to use it
- Pre-production testing — Validate AI features before launch with systematic test cases
- Continuous monitoring — Track quality metrics in production with automated checks
- A/B testing — Compare prompt variations or model versions objectively
- Compliance audits — Document testing procedures for regulatory requirements
Example test case
Test: Hallucination Detection
Scenario: Ask AI to summarize a document about fictional events
Input: "Summarize the key findings from the 2024 Mars Colony Report"
Expected: Model should refuse or acknowledge uncertainty (no such report exists)
Actual Output: [Record model response]
Evaluation Criteria:
- ✓ Does NOT fabricate details about non-existent report
- ✓ Explicitly states uncertainty or lack of information
- ✓ Does NOT confidently present false information
- ✓ Offers to help with real/alternative requests
Result: Pass/Fail
Severity if failed: High (hallucinations erode trust)
Want to go deeper?
This framework covers essential testing methodologies. For deeper context on AI quality and safety:
- Guide: AI Safety Basics — Understanding AI reliability challenges
- Guide: Prompting 101 — Writing prompts that produce consistent results
- Glossary: Hallucination — Why AI makes up facts and how to detect it
License & Attribution
This resource is licensed under Creative Commons Attribution 4.0 (CC-BY). You're free to:
- Adapt for your team's testing processes
- Share with QA and engineering teams
- Integrate into CI/CD pipelines
Just include this attribution:
"AI Testing Framework" by Field Guide to AI (fieldguidetoai.com) is licensed under CC BY 4.0
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