<p>Evaluating the quality of long-form answers generated by Question Answering systems presents significant challenges. Traditional metrics, such as BLEU and ROUGE, often reduce the assessment to a single similarity score with a reference answer, failing to capture semantic and specific aspects of answer quality. This reliance on an aggregated score not only overlooks important dimensions but also depends heavily on the availability of reference answers, which may not always be practical or sufficient. Developing metrics capable of individually assessing specific criteria, particularly completeness and relevance, is crucial for identifying weaknesses and guiding improvements in these systems. To address these limitations, this paper introduces specialized metrics designed to evaluate completeness and relevance of long answers without the need for reference texts. We present a new dataset comprising long answers to instructional questions in Computer Science, annotated by human experts based on completeness and relevance. Building upon this, we propose three novel metric models: (1) a prompt-based strategy utilizing Large Language Models to assess answers, (2) an approach that adapts precision and recall concepts by segmenting answers into discrete information units, and (3) a regression model trained on synthetic data to predict completeness and relevance scores. Experimental results demonstrate that the proposed metrics closely align with human judgments and provide more detailed evaluations of completeness and relevance compared to traditional metrics. By enabling a more granular assessment, these metrics facilitate targeted refinements in QA systems, enhancing their ability to meet users’ informational needs more effectively.</p>