A/B Testing
Scientifically test different prompts, models, and configurations to maximize AI performance.
The Challenge
❌ Without A/B Testing:
- • Guessing which prompt works best
- • No data to back up decisions
- • Risky "big bang" deployments
- • Wasted money on wrong models
✓ With RAG Engine A/B Testing:
- • Data-driven prompt optimization
- • Statistical confidence in results
- • Safe gradual rollouts
- • Proven ROI on model choices
What Can You Test?
Run experiments on any aspect of your AI configuration
Prompt Variations
Test "You are a helpful assistant" vs "You are an expert in our product" to see which performs better.
Model Comparison
Compare GPT-4 vs Claude 3 vs Gemini for your specific use case.
Retrieval Settings
Test top-5 vs top-10 document retrieval to optimize accuracy.
How It Works
Built-in experimentation framework
Multiple Variants
Test different prompts, models, or configurations side by side with traffic splitting.
Statistical Significance
Automatic significance testing tells you when results are conclusive.
Custom Metrics
Track accuracy, latency, user satisfaction, or any custom metric you define.
Gradual Rollout
Start with 1% traffic and gradually increase as confidence grows.
Use Cases
Optimize Prompts
Find the perfect prompt through systematic testing.
Compare Models
Choose the best LLM for your use case.
Tune Configuration
Optimize chunk size, overlap, and retrieval settings.
Safe Rollouts
Deploy changes safely with gradual traffic increases.
Comparison
Only RAG platform with native experimentation
| Feature | RAG Engine | LangChain | LlamaIndex | Pinecone |
|---|---|---|---|---|
| Built-in A/B testing | ✓ | ✗ | ✗ | ✗ |
| Statistical significance | ✓ | ✗ | ✗ | ✗ |
| Traffic splitting | ✓ | ✗ | ✗ | ✗ |
| Multi-variant tests | ✓ | ✗ | ✗ | ✗ |
| Custom metrics | ✓ | ~ | ~ | ✗ |
Works Great With
Ready to Get Started?
Start optimizing your AI with A/B testing today.
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