<p>Generative Large Language Models (LLMs) have shown promising results in text annotation tasks, which is of interest to social scientists. The most commonly used approaches, zero-shot and few-shot learning, do not sufficiently exploit the in-context learning capabilities of these models. Extant work demonstrates that allowing these models to retain memory throughout the annotation task increases performance considerably. In this article, we propose a refinement to the memory approach from Timoneda et al. (Memory Is All You Need: Testing How Model Memory Affects LLM Performance in Annotation Tasks. arXiv preprint: 2503.04874, 2025) that leads to significant performance gains over the original version. We implement a two-run method where we keep 100 highly informative examples from the first run in the memory at the start of a second run. During the first run, the model improves its performance as it progresses through the annotation task, a phenomenon we attribute to in-context learning. Then, during the second run, performance is high and consistent throughout the task, improving by a further 3.56% over the .standard memory approach and a remarkable 13.26% over few-shot learning with chain-of-thought reasoning, the current gold standard. These results have important implications for applied researchers looking to improve measurement accuracy in annotation tasks</p>