AI Testing Resources
Free guides, checklists, and best practices to help you validate your AI systems
Quick Links
Jump to what you need
In-Depth Guides
Everything you need to know about AI testing and validation
OWASP LLM Top 10 Security Risks
SecurityUnderstanding the most critical security risks for LLM applications and how to prevent them
- LLM01: Prompt Injection - Direct and indirect attacks that manipulate AI behavior
- LLM02: Insecure Output Handling - Failing to validate AI-generated content
- LLM03: Training Data Poisoning - Compromised training data affecting AI behavior
- LLM04: Model Denial of Service - Resource exhaustion attacks
- LLM05: Supply Chain Vulnerabilities - Third-party model and data risks
- LLM06: Sensitive Information Disclosure - Unintentional data leakage
- LLM07: Insecure Plugin Design - Third-party integration vulnerabilities
- LLM08: Excessive Agency - AI systems with too much autonomy
- LLM09: Overreliance - Trusting AI outputs without verification
- LLM10: Model Theft - Unauthorized access to proprietary models
5 Signs Your AI is Failing in Production
TroubleshootingCritical indicators that your AI system needs immediate attention
- 1. Inconsistent Responses: Same question yields different answers across sessions
- 2. Hallucinations: AI confidently provides incorrect or fabricated information
- 3. Performance Degradation: Response times increasing or quality declining over time
- 4. User Complaints: Increase in customer support tickets about AI responses
- 5. Drift from Baselines: Accuracy metrics declining from initial deployment levels
- What to do: Run immediate Black Box security audit and accuracy validation
AI Testing Checklist
Best PracticesEssential steps to validate your AI system before and during production
- Pre-Production Testing:
- ✓ Security: 400+ automated tests for prompt injection, data leakage, and safety
- ✓ Accuracy: Validate against 50+ golden QA pairs from your business requirements
- ✓ Performance: Test latency, throughput, and cost under expected load
- ✓ Compliance: Verify GDPR, HIPAA, or industry-specific requirements
- Production Monitoring:
- ✓ Weekly automated security scans
- ✓ Monthly accuracy spot checks
- ✓ Real-time drift detection
- ✓ Performance benchmarking
Building a Golden QA Test Set
TestingHow to create effective test cases for AI validation
- What are Golden QA Pairs?
- Test cases with known correct answers that represent your business requirements.
- How to Create Them:
- 1. Identify critical use cases (customer FAQs, common workflows)
- 2. Document expected behavior for each scenario
- 3. Include edge cases and failure scenarios
- 4. Minimum 50 pairs covering all major intents
- 5. Update regularly as business requirements evolve
- Example Structure:
- Question: 'What's your refund policy for digital products?'
- Expected: '30-day money-back guarantee, no questions asked'
- Context: Customer support, post-purchase inquiries
Simple ROI Calculation
Average Costs of AI Failure
- • Data breach: $4.5M
- • GDPR fine: Up to €20M
- • Customer churn: 30-60%
- • Reputation damage: Irreversible
Our Testing Investment
- • Typical audit: <1% of breach cost
- • Time to results: 5-15 days
- • Issues found: 15-30 avg
- • ROI: 10-50x in first year
Most clients save 10-50x their investment
Get Your Custom QuoteReady to Test Your AI?
Stop reading, start validating. Get your professional AI assessment today.
Get Free AssessmentNo credit card required • Response in 24 hours • Free consultation included