The HumanEval dataset and pass@k metric have revolutionized how we measure the performance of LLMs in code generation tasks. HumanEval is a hand-crafted dataset consisting of 164 programming challenges, each with a function signature, docstring, body, and several unit tests. Traditional evaluation methods for generated code involved comparing the produced solution with the ground-truth code using metrics like BLEU score, which measure text similarity rather than functional correctness. The pass@k metric addresses this limitation by evaluating the probability that at least one of the top k-generated code samples for a problem passes the unit tests, aligning more closely with the practices of human developers and providing a valuable benchmark for the ongoing development of code generation models.