RAG Retrieval Precision & Recall Evaluator

RETRIEVAL METRIC PARAMETERS

Total Retrieved Chunks (Top-K)

Relevant Chunks Retrieved

Total Ground Truth Relevant Chunks in Knowledge Base

RETRIEVAL QUALITY SCORECARD

80.0%

Precision@K

80.0%

Recall@K

0.80

F1 Score

Context Quality & Noise Breakdown

Context Noise Ratio (Irrelevant Chunks): 20.0% (2 noise chunks)

Missed Relevant Chunks: 2 chunks missed

Retrieval Status Recommendation: ✓ High Quality Retrieval

RAG Retrieval Quality Guide — Optimizing Vector Search Pipelines

Retrieval-Augmented Generation (RAG) performance depends directly on the quality of retrieved context. If vector search returns irrelevant noise or misses critical ground-truth facts, the LLM will generate incomplete or inaccurate responses.

Key RAG Retrieval Evaluation Metrics

Precision@K: Measures context cleanliness (`Relevant / Top-K`). High precision prevents prompt pollution.
Recall@K: Measures context completeness (`Relevant / Total Ground Truth`). High recall prevents missed facts.
F1 Score: Harmonic mean of Precision@K and Recall@K (`2 * (P * R) / (P + R)`).

Related RAG & AI Tools

Calculate RAG chunk sizes with our RAG Chunk Size Calculator, estimate context window usage with the Context Window Calculator, or count model tokens with the Multi-Model Token Counter.

Frequently Asked Questions

Common questions about this tool.

What is RAG Retrieval Evaluation? ▼

RAG (Retrieval-Augmented Generation) evaluation measures how accurately a vector database retrieves relevant context chunks for a given query before feeding them to an LLM.

What is Precision@K in vector retrieval? ▼

Precision@K calculates the percentage of the top K retrieved chunks that are actually relevant to the query (`Relevant Retrieved / Total Retrieved`).

What is Recall@K in vector retrieval? ▼

Recall@K measures the percentage of all ground-truth relevant document chunks that were successfully retrieved (`Relevant Retrieved / Total Ground Truth`).

Why is high context noise bad for LLMs? ▼

Irrelevant chunks included in the LLM prompt increase token costs and cause the "Lost in the Middle" phenomenon, where LLMs miss critical facts buried in noise.

How do I improve RAG retrieval precision and recall? ▼

Optimize chunk sizes (256–512 tokens with 10% overlap), implement hybrid search (dense embeddings + BM25 keyword search), and use cross-encoder re-rankers.