Synthetic Data Generation — Machine Learning Datasets

This project is a comprehensive educational resource and knowledge base dedicated to the development and application of large language models and autonomous agentic systems. It provides a structured framework for understanding prompt engineering, context management, and the architectural patterns required to build task-oriented AI. The repository serves as a central hub for learning how to design, evaluate, and optimize interactions with language models, ranging from basic prompting techniques to complex, multi-step reasoning workflows. The guide distinguishes itself through its focus on agentic orchestration and advanced context engineering. It details methodologies for dynamic task decomposition, where complex queries are broken into manageable subtasks, and hierarchical context engineering, which structures instructions to manage agent behavior and domain-specific knowledge. Furthermore, it covers the integration of external tools through function calling and the implementation of stateful memory systems to track task progress and execution history. Beyond core prompting strategies, the repository covers a broad capability surface including retrieval-augmented generation, synthetic data generation, and automated evaluation using model-based verification. It also provides technical documentation and benchmarks for a wide array of proprietary and open-source models, alongside practical guidance on mitigating security risks such as prompt injection and jailbreaking. The documentation is maintained as an open-source repository, offering a collection of guides, research paper summaries, and interactive notebooks to support hands-on learning.