Unverified50% confidenceSolutionExact time
For small RAG applications, first get things working with hand-written cosine similarity, and only consider introducing a vector database when the score gap and scale genuinely require it.
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50%
Confidence
Long-term
Relevance
10/17/2026
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Related Claims
VerifiedTraditional RAG relies on cosine similarity or dot product operations to match text embedding vectors, has limited capability in handling multi-hop questions, and is prone to causing large models to generate hallucinated content.75% similarUnverifiedIn actual RAG use, chunking strategy, embedding model choice, and whether to add keyword filtering often determine the results more than the database itself.75% similarUnverifiedTaking RAG as an example, interviewers will follow up by asking why Fine-tuning was not used, how to optimize when retrieval recall rate is low, and which vector database was used and why.74% similarUnverifiedThe granularity, overlap, and metadata carry-along of text chunking strategies can significantly affect RAG recall performance74% similarUnverified相比RAG提交prompt有硬上限，图谱工具调用允许模型按需调用任意次数以挖掘所需信息73% similar
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