Large　language　models　(LLMs)　can　generate　personas　based　on　prompts　that　describe　the　target　user　group.　To　understand　what　kind　of　personas　LLMs　generate,　we　investigate　the　diversity　and　bias　in　450　LLM-generated　personas　with　the　help　of　internal　evaluators　(n=4)　and　subject-matter　experts　(SMEs)　(n=5).　The　research　findings　reveal　biases　in　LLM-generated　personas,　particularly　in　age,　occupation,　and　pain　points,　as　well　as　a　strong　bias　towards　personas　from　the　United　States.　Human　evaluations　demonstrate　that　LLM　persona　descriptions　were　informative,　believable,　positive,　relatable,　and　not　stereotyped.　The　SMEs　rated　the　personas　slightly　more　stereotypical,　less　positive,　and　less　relatable　than　the　internal　evaluators.　The　findings　suggest　that　LLMs　can　generate　consistent　personas　perceived　as　believable,　relatable,　and　informative　while　containing　relatively　low　amounts　of　stereotyping.