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Workflows for preparing and generating data for retrieval-augmented generation.
Distinguishing note: Focuses on the pipeline aspect of RAG data preparation.
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This project is a comprehensive educational resource and technical guide focused on the development, optimization, and application of large language models. It provides a structured curriculum for mastering prompt engineering, ranging from foundational principles of instruction design to advanced techniques for improving model reasoning, accuracy, and reliability. The guide distinguishes itself by offering deep technical insights into agentic workflows and autonomous system design. It covers the implementation of multi-step reasoning chains, tool integration through function calling, and stat
Addresses the common challenge of data scarcity in RAG projects through synthetic generation.