This patent presents novel data generation techniques specifically developed for tool-based Large Language Model applications. The invention addresses the challenge of creating high-quality training data for LLMs that need to interact with external tools and APIs. The patented methods include automated data generation pipelines capable of producing 6.4 lakh tokens of high-quality fine-tuning data using multi-agent systems. The technology incorporates advanced prompting techniques, code-based approaches that reduce token size by 50%, and novel type-check approaches based on graph structures. The system enables scalable data generation while maintaining quality and reducing operational costs for tool-augmented LLM training.