LlamaIndex RAG evaluation splits retrieval and generation quality

The article discusses how to effectively evaluate the quality of retrieval-augmented generation (RAG) systems, particularly when using LlamaIndex. It emphasizes the importance of separating the evaluation into two distinct phases: assessing the retriever's ability to find relevant documents and evaluating the generator's faithfulness to the retrieved context. The author highlights that without visibility into the specific document fragments used to generate an answer, it's impossible to pinpoint whether an error stems from poor retrieval or flawed generation. LlamaIndex offers tools like RetrieverEvaluator, FaithfulnessEvaluator, and RelevancyEvaluator to address these separate aspects, though they rely on LLM judgments and do not entirely replace manual verification. AI

IMPACT Improves RAG system reliability by enabling granular evaluation of retrieval and generation components.

RANK_REASON The item details specific tools and techniques for evaluating RAG systems using LlamaIndex.

Read on dev.to — LLM tag →

• FaithfulnessEvaluator
• LlamaIndex
• OpenAI
• RelevancyEvaluator
• retrieval-augmented generation
• RetrieverEvaluator

• product
• other

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