GISTEmbed is a novel framework designed to enhance the selection of in-batch negatives during contrastive training for text embedding models. Traditional unsupervised triplet mining, while automating training data generation, can introduce biases and noise, degrading model performance. GISTEmbed addresses this issue by integrating a guide model to dynamically select negative samples during training, reducing reliance on random sampling and equal utility assumptions of batch negatives. This approach significantly reduces noise from data quality issues and improves model fine-tuning. Benchmarked against the Massive Text Embedding Benchmark (MTEB), GISTEmbed demonstrates consistent performance improvements across various model sizes and achieves state-of-the-art results in select categories. The framework leverages the capabilities of large, high-performing guide models to augment the training efficiency and effectiveness of smaller models, making advanced AI technologies more accessible and cost-effective, especially for resource-constrained applications. The paper also discusses the potential limitations and future directions, highlighting the importance of careful dataset selection and the need for further research to address biases in guide models.GISTEmbed is a novel framework designed to enhance the selection of in-batch negatives during contrastive training for text embedding models. Traditional unsupervised triplet mining, while automating training data generation, can introduce biases and noise, degrading model performance. GISTEmbed addresses this issue by integrating a guide model to dynamically select negative samples during training, reducing reliance on random sampling and equal utility assumptions of batch negatives. This approach significantly reduces noise from data quality issues and improves model fine-tuning. Benchmarked against the Massive Text Embedding Benchmark (MTEB), GISTEmbed demonstrates consistent performance improvements across various model sizes and achieves state-of-the-art results in select categories. The framework leverages the capabilities of large, high-performing guide models to augment the training efficiency and effectiveness of smaller models, making advanced AI technologies more accessible and cost-effective, especially for resource-constrained applications. The paper also discusses the potential limitations and future directions, highlighting the importance of careful dataset selection and the need for further research to address biases in guide models.