The following targeted strategies can be used when optimizing the RAG system based on the results of the Ragas assessment:
- Low Faithfulness Score
- Checking the search engine: ensuring that relevant and comprehensive information is returned
- Adjusting the generator prompt: emphasizing context-based answers
- Increased fact-checking mechanisms
- Low answer_relevancy score
- Optimizing the Query Comprehension Module
- Improvement of the generator's problem-focusing capabilities
- Consider adding answer post-processing steps
- Low context_relevancy score
- Adjusting the retrieval query extension strategy
- Optimizing vectorized model selection
- Improved screening mechanisms for recall results
Best practices include:
- build upAssessment benchmarks: Tracking changes in key indicators
- adoptionIterative optimization: Adjust one component at a time
- carry outcontrol experiment: Compare metric performance before and after optimization
- emphasizeBalance of indicators: Avoid over-optimization of single indicators
Through these methods, developers can systematically improve the overall performance of RAG systems.
This answer comes from the articleRagas: assessing RAG recall QA accuracy and answer correlationThe
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