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Test a RAG system's ability to provide concise answers through prompt engineering and parameter optimization.

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LLM_RAG_Learning_Kant

The objective was to test a RAG system's ability to provide concise answers through prompt engineering and parameter optimization. Using four influential works of Immanuel Kant (a challenging domain), I assessed the system's contextual awareness and retrieval capabilities. The tests involved two sets of questions: direct definition queries and complex analysis questions requiring long-span context understanding.

The texts used for the analysis are (obtained from the Project Gutenberg data repository):

Book_1: Kant's Critique of Judgement
Book_2: The Critique of Practical Reason
Book_3: The Critique of Pure Reason
Book_4: Fundamental Principles of the Metaphysic of Morals

For more information, visit my Portfolio: https://jelinr.github.io/

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Test a RAG system's ability to provide concise answers through prompt engineering and parameter optimization.

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