Evaluating RAG pipelines with the RAG triad
Retrieval-Augmented Generation (RAG) emerged as a dominant framework for feeding Large Language Models (LLMs) the context beyond the scope of their training data and enabling LLMs to respond with more grounded answers and fewer hallucinations based on that context.
However, designing an effective RAG pipeline can be challenging. You need to answer questions such as:
How should you parse and chunk text documents for vector embedding? What chunk size and overlay size should you use?
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