LucaStrano/Experimental_RAG_Tech
A collection of experimental Retrieval Augmented Generation (RAG) Techniques to elevate your pipelines, all with code and intuitive explanations
This project offers experimental techniques to improve Retrieval Augmented Generation (RAG) systems. It helps developers and AI engineers optimize how relevant information is found and presented to a Large Language Model. You input documents and queries, and it provides methods to get more accurate and efficient retrieval results, leading to better AI responses.
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Use this if you are a developer or AI engineer looking to experiment with novel, efficiency-focused methods to enhance the retrieval component of your RAG applications.
Not ideal if you need thoroughly tested, production-ready solutions, as these techniques are experimental and may require further validation.
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Jupyter Notebook
License
MIT
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Last pushed
Jul 21, 2025
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