Overview: The A/B Testing Engine
ABExperiment is Knobs’ testing and validation module, built to let teams execute organized A/B, multivariate, and contextual experiments on data signals, AI outputs, user interfaces, and personalization approaches.
Mission: Facilitate automated, data-driven testing to identify optimal configurations for accuracy and user experience.
ABExperiment connects creation (Kreate) and diagnostics (KnobsScope) — guaranteeing every data product, AI assistant, or interface upgrade is measured by actual results.
Core Features
- 🧠 Experiment Orchestration: Design and execute A/B and multivariate tests for models, prompts, or features.
- ⚙️ Dynamic Parameter Knobs: Adjust hyperparameters, prompts, or routing strategies without redeployment.
- 📈 Automatic Experiment Tracking: Logs metrics, versions, and configurations for reproducibility.
- 🧩 Integrated with LLM & ML Pipelines: Works with OpenAI, Hugging Face, or custom APIs.
- 📊 Statistical Significance Engine: Built-in Bayesian or frequentist methods for measuring lift and confidence.
- 🔄 Continuous Experimentation: Automate iterative testing with feedback loops from production telemetry.
Key Modules
1. Experiment Builder
A guided setup for hypotheses, variants, metrics, and sample sizes, featuring templates for website UX, AI prompts, and model comparisons.
2. Experiment Runtime Engine
Manages traffic routing, data sampling, and metrics during experiments, ensuring fairness, isolation, and reproducible outcomes.
3. Analytics & Significance Layer
Calculates conversion lift, confidence intervals, and statistical power, with support for frequentist and Bayesian approaches.
4. Cross-Product Connector
Allows experiments to run across multiple Dataknobs layers, including:
- KreateDataProducts: Test algorithms or KPI calculations.
- KreateWebsites: Test headlines, layouts, or content tone.
- KreateBots: Test prompts, LLM models, and personalization strength.
5. Continuous Optimization Loop
Works seamlessly with KnobsScope diagnostics and tuning agents. Automatically recommends new variants or settings to test, informed by earlier outcomes.
Key Benefits
- Scientific Validation: Quantify impact before deploying any data or AI change.
- Unified Across Layers: One system for AI, data, and UX experiments.
- Automated Workflow: Define, run, analyze, and iterate — all in one loop.
- Governance Ready: Integrates with Kontrols for safe and compliant testing.
- Human + AI Collaboration: Combine quantitative metrics with qualitative review.
- Continuous Optimization: Auto-learns from results to improve next experiments.