How I Test AI Tools
No 30-minute reviews. No fake scenarios. No corporate influence. Here's exactly how I separate the gold from the garbage.
The Problem with Most "Reviews"
90% of AI tool "reviews" are complete BS. They're either written by people who spent 20 minutes clicking around, or they're paid promotional content disguised as honest evaluation.
❌ What Others Do
- ×30-minute "reviews" based on marketing demos
- ×Affiliate links without disclosure
- ×Copy-paste marketing claims as "features"
- ×Never mention serious limitations
✅ What I Do
- ✓Minimum 2 weeks of real-world testing
- ✓Zero financial incentives from companies
- ✓Test with actual projects and deadlines
- ✓Document every failure and limitation
The CK Testing Methodology
1
Initial Assessment (Week 1)
Setup & First Impressions
- • Account creation and onboarding experience
- • Interface usability and learning curve
- • Initial feature exploration
- • Performance on basic tasks
Reality Check
- • Compare actual features vs marketing claims
- • Test edge cases and failure scenarios
- • Document initial limitations
- • Measure baseline performance metrics
2
Deep Dive Testing (Week 2)
Real Project Integration
- • Use tool for actual client work (when possible)
- • Test with realistic data volumes
- • Evaluate workflow integration
- • Measure productivity impact
Stress Testing
- • Push tool to its limits
- • Test with complex, ambiguous inputs
- • Evaluate error handling
- • Document breaking points
3
Extended Evaluation (Week 3+)
Long-term Viability
- • Consistency over time
- • Learning curve and skill development
- • Support responsiveness
- • Update frequency and quality
Competitive Analysis
- • Direct comparisons with alternatives
- • Value proposition assessment
- • Unique advantage identification
- • Cost-benefit analysis
What I Actually Measure
📊 Performance Metrics
- • Response times (P50, P95, P99)
- • Accuracy rates across different tasks
- • Error rates and failure modes
- • Uptime and availability
- • Resource usage (API calls, credits)
💰 Cost Analysis
- • Cost per task/request
- • Hidden fees and overages
- • Free tier limitations
- • Pricing tier value comparison
- • ROI for different use cases
🎯 User Experience
- • Learning curve steepness
- • UI/UX friction points
- • Documentation quality
- • Support response times
- • Community helpfulness
My Testing Environment
Hardware & Software
- • MacBook Pro M3 Max (primary)
- • Windows 11 workstation (compatibility testing)
- • Various mobile devices
- • Multiple browsers and environments
- • Network condition simulation tools
Testing Scenarios
- • Individual user workflows
- • Team collaboration scenarios
- • Enterprise-scale usage patterns
- • Mobile and offline usage
- • API and integration testing
Why This Level of Testing Matters
Because your time and money are valuable. Because bad AI tools can waste weeks of work and thousands of dollars. Because someone needs to do the real work of separating legitimate innovation from marketing fluff.
I test so you don't have to.
That's what champions do.