Failure Modes of RAG
As you work with Retrieval-Augmented Generation (RAG) systems, it is crucial to recognize and address their most common failure modes. These pitfalls can undermine the effectiveness of your pipeline and lead to poor user experiences or unreliable outputs. Three primary failure modes to watch for are poor chunking, dense retrieval mismatch, and embedding drift.
Poor chunking occurs when documents are split into segments that are too large, too small, or semantically incoherent. If your chunks do not align with natural topic boundaries, the retriever may return irrelevant or fragmented information, degrading the quality of the generated answer.
Dense retrieval mismatch refers to situations where the retrieval model selects chunks that are not truly relevant to the query, even though they may appear similar in the embedding space. This often happens when the retriever's training data or objective does not match your domain or user intent, causing it to prioritize superficially similar but contextually irrelevant passages.
Embedding drift emerges over time as the underlying embedding models or the indexed corpus change. If embeddings used for retrieval no longer reflect the current meaning of queries or documents, the retriever may fail to surface the most relevant content, leading to outdated or off-topic results.
Irrelevant context injection can significantly distort the outputs of a RAG system. When the retriever supplies passages that do not pertain to the user's query, the language model may incorporate unrelated facts or hallucinate plausible-sounding but incorrect information. This undermines trust in the system and can be especially problematic in high-stakes applications.
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As you work with Retrieval-Augmented Generation (RAG) systems, it is crucial to recognize and address their most common failure modes. These pitfalls can undermine the effectiveness of your pipeline and lead to poor user experiences or unreliable outputs. Three primary failure modes to watch for are poor chunking, dense retrieval mismatch, and embedding drift.
Poor chunking occurs when documents are split into segments that are too large, too small, or semantically incoherent. If your chunks do not align with natural topic boundaries, the retriever may return irrelevant or fragmented information, degrading the quality of the generated answer.
Dense retrieval mismatch refers to situations where the retrieval model selects chunks that are not truly relevant to the query, even though they may appear similar in the embedding space. This often happens when the retriever's training data or objective does not match your domain or user intent, causing it to prioritize superficially similar but contextually irrelevant passages.
Embedding drift emerges over time as the underlying embedding models or the indexed corpus change. If embeddings used for retrieval no longer reflect the current meaning of queries or documents, the retriever may fail to surface the most relevant content, leading to outdated or off-topic results.
Irrelevant context injection can significantly distort the outputs of a RAG system. When the retriever supplies passages that do not pertain to the user's query, the language model may incorporate unrelated facts or hallucinate plausible-sounding but incorrect information. This undermines trust in the system and can be especially problematic in high-stakes applications.
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