rag-in-action

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Basic Information

This repository is a practical training resource and companion for a RAG (Retrieval-Augmented Generation) training camp. It is presented as a systematic, hands-on overview that breaks RAG down into ten major components and provides four practical projects to learn the end-to-end RAG workflow. The stated intention is to teach practitioners how to build and deploy RAG solutions for real business problems rather than applying business to fit a preselected RAG architecture. The material focuses on detailed investigation and careful tuning of RAG systems, emphasizing scenario-specific adjustments, optimizations and customizations. The repo targets engineers and teams who want a comprehensive understanding of RAG principles and the practical know-how to adapt RAG to different application needs.

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Features
Comprehensive decomposition of RAG into ten core components, enabling stepwise study of architecture and roles within the RAG pipeline. Four hands-on projects designed to cover the full RAG workflow from data retrieval to response generation, intended to provide practical experience. Emphasis on business-oriented application and the need to tailor models and systems to specific scenarios rather than generic solutions. Focus on optimization and customization practices, with deep dives into detailed implementation considerations. Educational orientation meant to bridge theory and practice for practitioners aiming to productionize RAG solutions.
Use Cases
The repository helps practitioners and engineering teams understand how RAG works in practice and how to apply it to business problems. By breaking RAG into ten components and offering four practical projects, it provides a structured learning path for building end-to-end systems and for identifying which parts need tuning for particular use cases. The focus on scenario-specific adjustments and optimization supports teams in avoiding one-size-fits-all designs and in making RAG solutions more effective in production. The materials encourage deep attention to detail, which is useful for robust implementation and customization in real-world deployments.

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