Dimensions for Creating Retrieval Augmented Generation (RAG) Pipelines
Overview
In the realm of Artificial Intelligence, advanced models like Retrieval Augmented Generation (RAG) have gained significant attention. However, it’s crucial to prioritize the evaluation of these models before integrating complex features.
Assessment Nuances
It’s vital to carefully assess RAG pipelines, considering both retrieval and generation dimensions. Researchers and practitioners should prioritize strengthening the evaluation setup to ensure reliable and robust performance.
Retrieval Dimensions
- Context Precision: Determines the priority ranking of ground-truth items in the context.
- Context Recall: Assesses the correspondence between the ground-truth response and the retrieved context.
- Context Relevance: Evaluates the relevance of the offered contexts.
- Context Entity Recall: Measures the recall of the retrieved context compared to ground truths.
- Noise Robustness: Assesses the model’s ability to handle question-related noise documents.
Generation Dimensions
- Faithfulness: Evaluates the factual consistency of the generated response.
- Answer Relevance: Calculates how well the generated response responds to the given question.
- Negative Rejection: Assesses the model’s capacity to hold off on responding when documents don’t include enough information.
- Information Integration: Evaluates the model’s ability to integrate data from different documents to provide answers to complex questions.
- Counterfactual Robustness: Assesses the model’s ability to recognize and ignore known errors in documents.
Frameworks
- Ragas: https://docs.ragas.io/en/stable/
- TruLens: https://www.trulens.org/
- ARES: https://ares-ai.vercel.app/
- DeepEval: https://docs.confident-ai.com/docs/getting-started
- Tonic Validate: https://docs.tonic.ai/validate
- LangFuse: https://langfuse.com/
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