Introduction to RAG Evaluation
The Role of RAG Evaluation in Vectorize
Vectorize offers a powerful set of tools to help users conduct RAG evaluations with ease, ensuring you can identify the best-performing strategy before committing to building a RAG pipeline. This proactive approach helps you avoid the common mistakes that lead to hallucinations or poor system performance.
Core Features of Vectorize's RAG Evaluation:
Customizable Evaluations: With Vectorize, you can test different embedding models, chunking strategies, and retrieval configurations in a controlled environment. Upload your documents, configure various evaluation pipelines, and compare how each one performs.
Performance Metrics: Vectorize provides detailed evaluation metrics such as NDCG (Normalized Discounted Cumulative Gain) and relevancy scores. These metrics help identify the strategies that retrieve the most relevant information and generate the most accurate answers, ensuring you select the best option for your final pipeline.
Synthetic Question Generation: To evaluate the performance of each vectorization strategy, Vectorize generates synthetic questions based on the documents you upload. These questions serve as a benchmark to measure how well the retrieval and generation components are working together.
Real-Time Progress Monitoring: As your RAG evaluation progresses, you can monitor the performance of each strategy in real-time. This allows you to quickly identify and address any issues, ensuring a smooth evaluation process.
RAG Sandbox for Exploration: Once the evaluation is complete, Vectorize offers a RAG Sandbox where you can test different vector indexes and strategies with real queries. This hands-on exploration helps you understand how the system will perform in a real-world setting before committing to a pipeline.
From Evaluation to Pipeline
Once the RAG evaluation has identified the optimal configuration, you can then confidently build a RAG pipeline based on those findings. The key advantage of this approach is that it ensures your RAG pipeline is built on solid ground, having already proven its performance during the evaluation phase.
By using RAG evaluation first, you save yourself from the costly mistake of building a pipeline only to discover that it doesn't perform well or introduces hallucinations. This approach optimizes the efficiency and accuracy of your RAG system, ensuring that the final pipeline will be effective at retrieving and generating accurate, relevant, and contextually grounded responses.
Why Use Vectorize for RAG Evaluation?
Vectorize’s RAG evaluation solution provides an intuitive way to experiment with different strategies. It eliminates the need for writing custom scripts to try different configurations and performing ad-hoc data analysis to compare the results.
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