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 model tokens and context limits with our LLM BPE Tokenizer, estimate API inference expenses with the OpenAI Cost Estimator, or optimize prompt instructions with the Prompt Optimizer.

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.