Evaluation of large language models in a professional environment: Implementation, documentation, refactoring and code analysis

This paper presents a comparative analysis of
large language models used in professional
working environments to support everyday
knowledge-based tasks. The research focuses
on practical usage scenarios, including the
creation of technical documentation, software
development support, natural language processing,
and assistance in decision-making processes. The
evaluation is conducted through a set of defined
work tasks that reflect real working conditions,
with models assessed according to response
accuracy, consistency, clarity of generated content,
and adaptability to task context. The results
demonstrate noticeable differences among the
analyzed models in handling complex professional
queries and in the quality of generated outputs,
indicating that performance strongly depends on
the nature of the task. The analysis shows that
certain models exhibit a clear advantage across
most evaluation criteria, particularly in scenarios
requiring advanced contextual understanding and
technical reasoning. These findings emphasize
the importance of selecting an appropriate model
based on specific professional requirements and
work scenarios. This study contributes to a clearer
understanding of the practical applicability and
limitations of large language models in everyday
professional use.