RAG Pipeline Evaluator
Evaluate your RAG pipeline quality. Score your chunking, embeddings, retrieval, reranking, and context management to identify improvement opportunities.
- Free Evaluation
- RAG Best Practices
- Quality Score
RAG Pipeline Evaluator
Frequently asked questions
What is a RAG pipeline?
Retrieval-Augmented Generation (RAG) enhances LLM responses by first retrieving relevant documents from a knowledge base, then providing them as context to the model. It reduces hallucination and grounds responses in your data.
What chunk size should I use for RAG?
Start with 256-512 tokens for Q&A use cases and 512-1024 for summarization. The ideal size depends on your documents and queries — test multiple sizes and measure retrieval relevance.
Does reranking improve RAG quality?
Yes, significantly. Cross-encoder reranking typically improves answer relevance by 15-30% by re-scoring retrieved chunks using a more accurate (but slower) model before passing them to the LLM.
Should I use hybrid search?
Almost always yes. Hybrid search (combining vector similarity with keyword/BM25) outperforms pure vector search for most real-world queries, especially those containing specific terms, codes, or names.
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