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RAG & Vector Databases

Evaluating RAG Performance

Metrics like Faithfulness and Answer Relevance.

Interview: Medium - Important for production systems

How Do You Know Your RAG System Works?

RAG Evaluation Framework

Building a RAG pipeline is easy. Building one that works reliably is hard. Evaluation is the difference between a demo and a production system.

Key Metrics (RAGAS Framework)

  • Faithfulness: Does the answer only use information from the retrieved context? (Prevents hallucination)
  • Answer Relevancy: Does the answer actually address the user's question?
  • Context Precision: Are the retrieved chunks relevant to the question?
  • Context Recall: Did we retrieve all the chunks needed to answer?

Evaluation Approach

  1. Create a test set of question-answer-context triplets
  2. Run your RAG pipeline on the questions
  3. Score each response using the metrics above
  4. Use an LLM-as-judge for automated scoring at scale

Use Cases

Regression testing RAG pipelines after changes

Comparing different chunking strategies objectively

Identifying retrieval vs. generation failures

Setting quality baselines for production monitoring

Common Mistakes

Not evaluating at all — "it feels right" is not a metric

Only testing with easy questions that any approach would handle

Evaluating retrieval and generation together instead of separately

Using too small a test set (need 50+ diverse examples minimum)