As large language models (LLMs) continue to revolutionise artificial intelligence applications, the importance of high-quality data preparation has never been more critical. This webinar dives into the art and science of preparing datasets for effective LLM training, offering actionable insights for AI practitioners, data scientists, and engineers.We will explore the end-to-end process of data preparation, beginning with data collection strategies and progressing through cleaning, preprocessing, tokenisation, and annotation. Emphasis will be placed on identifying and mitigating biases, managing multilingual datasets, and ensuring data quality and diversity to enhance model performance. Real-world case studies will illustrate common pitfalls and solutions, while hands-on demonstrations will provide practical techniques for optimising datasets.Participants will gain a deeper understanding of how well-structured and curated data can significantly impact an LLM’s capabilities, reduce training costs, and improve ethical AI outcomes. Whether you are building LLMs from scratch or fine-tuning existing models, this session will equip you with the knowledge to leverage your data assets effectively.Join us to unlock the potential of data preparation and enable your LLMs to achieve unparalleled performance and generalisation.