Definition
In RAG pipelines, precision represents the proportion of retrieved documents or chunks that are truly relevant to the query. High precision ensures the LLM's context window is filled with pertinent information rather than 'noise,' thereby reducing the risk of hallucination and token wastage.
Not to be confused with floating-point numerical precision (FP16/32) or 'Recall' (finding all relevant items).
"A high-end espresso filter that allows only the pure essence of the bean through while blocking all bitter sediment."
- Recall(Inverse Trade-off: Increasing precision often limits the breadth of information found.)
- Reranking(Optimization Technique: Used to prune irrelevant results and boost precision before LLM generation.)
- Contextual Noise(Antonym: The presence of irrelevant data that high precision aims to eliminate.)
- Mean Reciprocal Rank (MRR)(Related Metric: Evaluates precision based on where the first relevant result appears.)
Conceptual Overview
In RAG pipelines, precision represents the proportion of retrieved documents or chunks that are truly relevant to the query. High precision ensures the LLM's context window is filled with pertinent information rather than 'noise,' thereby reducing the risk of hallucination and token wastage.
Disambiguation
Not to be confused with floating-point numerical precision (FP16/32) or 'Recall' (finding all relevant items).
Visual Analog
A high-end espresso filter that allows only the pure essence of the bean through while blocking all bitter sediment.