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How to build an automated RAG evaluation harness measuring Context Precision and Recall?

Intermediate · 2 min read · Evaluation & Testing

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Short answer: An automated RAG evaluation harness runs candidate retrieval pipelines across benchmark test queries, decomposing retrieved contexts and answers into atomic statements to compute Ragas metrics—Context Precision (rank signal-to-noise ratio), Context Recall (ground-truth coverage), Faithfulness (grounding), and Answer Relevance.

Detail

Evaluating RAG requires isolating retrieval performance from generation quality.

                  ┌──► 1. Context Precision ──► Are top-ranked retrieved chunks relevant?
Retrieval Phase ──┤
                  └──► 2. Context Recall    ──► Were all ground-truth facts retrieved?

                  ┌──► 3. Faithfulness       ──► Is generated output supported by context?
Generation Phase ─┤
                  └──► 4. Answer Relevance   ──► Does output directly answer the prompt?

Ragas Metric Definitions

  • Context Precision: Proportion of relevant chunks in top-KK vector search results.
  • Context Recall: Ratio of ground-truth reference statements present in retrieved context.
  • Faithfulness: Proportion of claims in generated output supported by retrieved context.

Example

Python RAG evaluation harness snippet using Ragas framework:

python
from ragas import evaluate
from ragas.metrics import faithfulness, answer_relevancy, context_recall, context_precision
from datasets import Dataset

# Sample evaluation dataset payload
eval_data = {
    "question": ["What is KV Cache?"],
    "contexts": [["KV Cache stores Key and Value tensors in GPU VRAM to avoid recomputation."]],
    "answer": ["KV Cache stores Key-Value states in GPU memory to speed up decoding."],
    "ground_truth": ["KV Cache stores key and value vectors in GPU memory during inference."]
}

dataset = Dataset.from_dict(eval_data)
# Run automated multi-metric evaluation
# results = evaluate(dataset, metrics=[faithfulness, answer_relevancy, context_recall, context_precision])

Interview tips

  • Walk through the Ragas evaluation trilemma.
  • Explain how Context Recall guides retriever chunking/hybrid search parameters while Faithfulness guides system prompt guardrails.

Related Concepts

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