Retrieval Quality
A measure of how well a RAG system's retrieval component surfaces the most relevant passages for a given query.
Retrieval quality determines the ceiling on answer quality in a RAG system — even the best language model cannot produce a correct answer if the relevant source passage is not retrieved. Retrieval quality is measured using standard information retrieval metrics: Precision@k (what fraction of the top-k retrieved passages are relevant?), Recall@k (what fraction of relevant passages appear in the top-k?), and Mean Reciprocal Rank (where does the first relevant passage appear in the ranking?).
Evaluating retrieval quality requires a ground-truth dataset: question-answer pairs where the relevant source passages are known. Building this evaluation dataset is time-consuming but essential for diagnosing retrieval failures and measuring the impact of pipeline changes. Common retrieval quality problems include: embedding models that perform poorly on domain-specific terminology, chunk sizes that split relevant information across boundaries, and metadata filtering that incorrectly excludes relevant documents.
More ai/ml Terms
Retrieval-Augmented Generation (RAG)
An AI architecture that combines information retrieval with text generation to produce answers grounded in source documents.
Vector Embedding
A numerical representation of text as a high-dimensional vector, enabling semantic similarity comparisons between passages.
BM25
A probabilistic keyword-ranking algorithm that scores documents by term frequency and inverse document frequency.
Chunking
The process of splitting large documents into smaller, overlapping segments optimized for retrieval and embedding.
Hallucination
When an AI model generates plausible-sounding but factually incorrect or fabricated information.
Large Language Model (LLM)
A neural network trained on massive text corpora that can understand and generate human language.
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