Dasha Metropolitansky, a research data scientist at Microsoft, developed Claimify, a system designed to extract claims from text. A claim is defined as a simple factual statement that can be verified as true or false. Claimify breaks down text into these claims, making it easier to evaluate content generated by language models. This process is particularly useful for hallucination detection, ensuring that language models answer questions based on source documents rather than fabricating information. Claimify also aids in assessing the relevance and comprehensiveness of answers by breaking them into individual factual claims, which can then be aggregated into a composite measure. The system operates by breaking down text into sentences and performing claim extraction on each sentence independently, using context to ensure accurate interpretation. Claimify's process involves three stages: selection, disambiguation, and decomposition. Selection filters out non-verifiable claims, disambiguation resolves or flags ambiguous claims, and decomposition breaks down sentences into standalone factual statements. This approach allows for more precise content evaluation, as demonstrated in examples involving complex sentences where Claimify successfully extracted relevant claims that baseline methods missed.