LLM factuality evaluation measures whether model outputs are accurate, grounded, complete, and safe for the intended domain. InfoBay builds expert-led evaluation loops that detect hallucinations, bias, weak reasoning, and unsafe responses before they reach production users.
Factuality is not a single score. Reliable evaluation needs domain-specific prompts, reference checks, reviewer calibration, error taxonomies, and feedback that can flow back into model improvement.
Layered checks for correctness, consistency, and safety.
Physicians, attorneys, researchers, engineers, and finance reviewers where needed.
Findings can inform SFT, RLHF, red-teaming, and monitoring.
InfoBay evaluates responses for factual correctness, citation quality, domain fit, harmful bias, refusal behavior, and stability across prompt variations.
Evaluation outputs are structured so model teams can identify failure patterns, prioritize fixes, and convert expert feedback into training or monitoring assets.
Answers for buyers
LLM factuality evaluation checks whether a model’s output is correct, grounded, complete, and safe for a specific use case or domain.
Yes. InfoBay supports expert review workflows for healthcare, legal, finance, and other high-stakes domains.
Yes. Evaluation findings can be converted into SFT, RLHF, DPO, red-team, and monitoring datasets.