LLM A/B Testing: Rigorous Experimentation for Organizations
The Problem
Every organization that runs experiments on user behavior, product features, or AI system outputs is engaged in applied research. The same methodological principles that govern academic research apply: sample size must be adequate, control conditions properly constructed, confounds identified, and results interpreted with statistical rigor. The most common failure mode is insufficient design — not insufficient technology. A/B tests launched without power analyses. Multiple comparisons without correction. Interaction effects ignored. Stopping rules informal.
Experimental Design Consulting
Formal hypotheses derived from theoretical frameworks, power analyses based on realistic effect size estimates, properly randomized assignment, pre-registered analytic plans, and results reporting with effect sizes, confidence intervals, and corrections for multiple comparisons. Whether you are running A/B tests on a consumer product, evaluating behavioral impact of a policy change, or testing AI system outputs against human baselines.
Multimodal Experimental Design
Complex experiments involving behavioral, physiological, and AI-generated data streams require specialized design expertise. When the outcome is a pattern of human responses across multiple modalities — reaction times, decision patterns, affective responses, behavioral sequences — the design must account for dependencies and interactions among these data streams.
AI System Evaluation
Organizations building AI systems that interact with humans face a specific challenge: the relevant outcome is not whether the system produces accurate outputs, but whether it produces outputs that humans respond to in the intended way. This requires designs that treat human-AI interaction as a psychological phenomenon — measuring perception, trust calibration, decision-quality under AI assistance, and downstream behavioral effects. A quantitative AI engineer with a doctorate in clinical psychology handles your engagement directly.
Patent-pending automated platform that quantifies how manipulable AI models are — before attackers do. Graduated manipulation testing across four proprietary dimensions, continuous sensitivity metrics, and court-admissible evidence chains with cryptographic verification. Empirically validated on production models. Continuous monitoring, EU AI Act and NIST AI RMF compliance documentation, and Daubert-standard forensic evidence.
Precision behavioral engineering for AI systems. Personality-engineered agents with measurable behavioral profiles from clinical frameworks, cryptographic chain of custody for every interaction, and behavioral compliance certification with statistical rigor for regulatory submissions.
The only A/B testing framework that isolates what actually matters. Shared candidate pools eliminate confounding between retrieval and ranking. Pre-registered experiments with cryptographic audit trails, real spelling errors from linguistic corpora, and multi-metric statistical rigor. Validated on 500 naturally occurring misspellings.