RAG Evaluation: Ragas Framework and Metrics
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Learn the RAG Triad evaluation metrics and how to use the Ragas framework for automated testing.
#ai-engineering#rag#evaluation#ragas#metrics
The RAG Triad Metrics
Evaluating LLMs is hard. For RAG systems, the RAG Triad framework splits evaluation into three distinct metrics:
- Context Relevance: Are the retrieved text chunks relevant to the user query? (Evaluates the Vector DB retrieval step).
- Faithfulness / Groundedness: Is the generated answer based only on the retrieved chunks, without introducing hallucinations? (Evaluates LLM output safety).
- Answer Relevance: Does the generated answer directly address the user's question?
Automated Evaluation with Ragas (Python)
python
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from ragas import evaluate
from datasets import Dataset
# Prepare test datasets
data = {
"question": ["What is the CAP Theorem?"],
"contexts": [["The CAP Theorem asserts that distributed systems choose Consistency or Availability."]],
"answer": ["The CAP theorem presents a tradeoff between Consistency and Availability."]
}
dataset = Dataset.from_dict(data)
# Run evaluation using LLM-as-a-judge metrics
# results = evaluate(dataset)
# print(results)Discussion
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