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Systematically Improving Your RAG


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Leveraging synthetic data to establish baseline performance metrics Extracting and utilizing metadata to enhance search results Combining full-text search and vector search for optimal retrieval Implementing user feedback mechanisms to gather valuable insights Analyzing user queries and feedback to identify improvement opportunities Prioritizing and implementing targeted improvements based on data-driven insights Continuously monitoring, evaluating, and retraining models as real-world data grows Exploring advanced techniques like query enhancement, summarization, and outcome modeling Through this step-by-step runbook, you'll gain practical knowledge on how to incrementally enhance the performance and utility of your RAG applications, unlocking their full potential to deliver exceptional user experiences and drive business value. To avoid this, generate a large set of synthetic questions and expected answers to evaluate your system's precision and recall before using real user data.

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