LLM Reliability
Lab
A systematic framework for evaluating how large language models perform on medical question answering — measuring accuracy, hallucination behavior, and prompt strategy effectiveness.
Platform Capabilities
Built for reproducible, rigorous evaluation of LLM reliability in high-stakes domains.
Multi-Model Comparison
Evaluate multiple LLM configurations side-by-side with unified metrics and statistical analysis.
Hallucination Detection
Automatically flag factual errors, fabricated information, and overconfident incorrect responses.
Prompt Strategy Testing
Compare zero-shot, structured, and chain-of-thought prompting across identical question sets.
Accuracy Metrics
Track accuracy, consistency scores, and confidence calibration with exportable research reports.
How It Works
Three steps from configuration to publishable research insights.
Configure Experiment
Select your model, prompting strategy, and the subset of medical QA samples to evaluate.
Run Evaluation
The pipeline sends questions to the LLM, collects responses, and scores them against ground truth.
Analyze Results
Explore accuracy metrics, hallucination cases, and model comparison charts in a research dashboard.
Why Reliability Matters
LLMs are increasingly used in clinical decision support, patient education, and drug information systems. A hallucinated diagnosis or incorrect dosage recommendation can have life-altering consequences.
This platform provides a systematic, reproducible methodology for measuring how reliably LLMs answer medical questions — and where they fail.
Read Ethics StatementReady to run your experiment?
Configure your model and prompt strategy, then evaluate LLM performance on curated medical QA benchmarks.
Start Experiment