Imagine teaching a brilliant but inexperienced AI assistant the intricacies of a new field, instantly. That's the promise of automated fine-tuning data generation for Large Language Models (LLMs). Fine-tuning, the process of tailoring a pre-trained LLM to a specific domain or task, traditionally relies on meticulously crafted query-response pairs. However, creating these pairs manually is a time-consuming and expensive bottleneck. New research introduces AUGCON, an innovative method that automates this process, generating diverse and high-quality data to supercharge LLMs. AUGCON tackles the challenge of capturing the nuances of context at different levels of detail, from specific facts to broad concepts. It starts by recursively deriving queries using a Context-Split-Tree (CST), ensuring that the generated questions cover the full spectrum of information within a given text. A smart scorer, trained through contrastive learning, then refines these queries, selecting the most relevant and diverse ones. Finally, a self-alignment and self-improving process ensures that the generated responses are not only accurate but also adhere to human values and preferred formats. This three-step process significantly boosts the efficiency and effectiveness of fine-tuning. Experiments show that LLMs trained with AUGCON-generated data outperform those trained with traditional methods, demonstrating improved accuracy and relevance across various benchmarks. This breakthrough opens doors to rapidly deploying custom LLMs in specialized fields like medicine, law, and finance, where access to high-quality, domain-specific data is often limited. While challenges remain in terms of handling complex contexts and mitigating potential biases, AUGCON represents a significant leap forward in unlocking the full potential of LLMs, paving the way for more powerful and versatile AI assistants.