Скачать или смотреть Session 7 rag evaluation with ragas and how to improve retrieval

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okay, let's dive deep into evaluating rag systems with ragas (retrieval augmented generation assessment) and explore techniques to improve retrieval. this will be a comprehensive guide covering everything from the basics of ragas to advanced optimization strategies.

*tutorial: rag evaluation with ragas & improving retrieval*

*i. introduction: why evaluate rag systems?*

rag (retrieval augmented generation) systems are becoming increasingly prevalent for tasks like question answering, content generation, and knowledge grounding. they work by first retrieving relevant documents from a knowledge base and then using those documents to generate a response. however, simply building a rag system doesn't guarantee that it will perform well. we need to evaluate its performance to:

*ensure accuracy:* verify that the generated answers are factually correct and aligned with the retrieved information.
*assess relevance:* confirm that the retrieved documents are indeed relevant to the query.
*measure completeness:* determine if the rag system provides comprehensive and thorough answers.
*identify bottlenecks:* pinpoint areas for improvement, such as the retrieval or generation stage.
*compare different rag pipelines:* objectively compare the performance of various rag configurations (e.g., different vector databases, different llms).

*ii. what is ragas?*

ragas is an open-source python library designed specifically for evaluating rag pipelines. it provides a set of metrics and tools to automatically assess the quality of retrieval and generation. ragas focuses on **interpretability**, meaning it tries to give you metrics that are easy to understand and act upon.

*key ragas metrics:*

*faithfulness:* measures how factually consistent the generated answer is with the retrieved context. a high faithfulness score indicates that the answer is well-supported by the retrieved documents.
*answer relevance:* evaluates how releva ...

#Session7 #RagEvaluation #softwaredevelopment
Session 7
RAG evaluation
Ragas
retrieval improvement
music information retrieval
performance metrics
melodic analysis
user feedback
database optimization
acoustic similarity
raga classification
machine learning
user experience
retrieval algorithms
content-based retrieval