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0Document Loaders1Chunking2Embedding Models3Vector Databases4Metadata5Retrievers6Hybrid Search7BM258Cross Encoder9Re-ranking10Query Expansion11Compression12Caching13RAG14LangChain Basics15ChromaDB16Streamlit17RAG Evaluation
RAG Engineering
Phase 3Module 18 of 18

RAG Evaluation

Without evaluation, you're flying blind — tweaking chunk size, swapping models, and adding re-rankers with no idea if changes help or hurt. Production RAG degrades silently as documents change, queries shift, and models update. Evaluation is the feedback loop that keeps quality high.

RAG evaluation is like a restaurant health inspection — you can't judge food safety by how the kitchen looks. You need standardized tests (metrics) run regularly to catch problems before customers do.

Visual Workflows

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Key Takeaways

  • 1.RAG evaluation systematically measures retrieval quality, answer faithfulness, and end-to-end system performance using labeled datasets and automated metrics — the only way to know if your pipeline actually works. Evaluation layers: (1) Retrieval metrics — precision@k, recall@k, MRR, nDCG.
  • 2.(2) Generation metrics — faithfulness (answer grounded in context?), relevance (answers the question?), correctness (matches ground truth). (3) End-to-end — LLM-as-judge, human evaluation.
  • 3.Frameworks: RAGAS (faithfulness, answer_relevancy, context_precision), DeepEval, custom eval sets. Build a golden dataset: 100-500 question-answer pairs with expected source documents.
  • 4.Run eval on every pipeline change. Track metrics over time in a dashboard.

Real Example

Scenario

Team changes chunk size from 512 to 1024. Eval suite runs: context_precision drops 8%, faithfulness unchanged, answer_relevancy up 3%. Decision: keep 512 — precision drop outweighs relevancy gain for their factual Q&A use case.

What you would do

In RAG Engineering, apply RAG Evaluation to this scenario: Team changes chunk size from 512 to 1024. Identify the inputs, run the technique, validate the output, and note one thing you would monitor in production.

Practice Task

Open the Code Walkthrough below and run it locally. Change one parameter related to RAG Evaluation (e.g. model, temperature, top_k, or tool name), observe the difference in output, and write 2–3 sentences explaining what changed.

Code Walkthrough

Highlighted lines show where RAG Evaluation happens in the code.

RAG Evaluation
1from ragas import evaluate  # import dependencies2from ragas.metrics import faithfulness, answer_relevancy, context_precision  # import dependencies3from datasets import Dataset  # import dependencies4
5eval_data = Dataset.from_dict({6    "question": ["What is the refund policy?", "How do I reset MFA?"],7    "answer": ["30-day refund window...", "Go to Settings > Security..."],8    "contexts": [["chunk1 text", "chunk2 text"], ["chunk3 text"]],9    "ground_truth": ["30 days", "Settings > Security > Reset MFA"],10})11
12results = evaluate(13    eval_data,14    metrics=[faithfulness, answer_relevancy, context_precision],15)16print(results)  # show output for debugging

Cheat Sheet

Quick recap

quick ref
  • •RAGAS: faithfulness + precision + relevancy
  • •Golden set: Q + A + source docs
  • •Fix retrieval before generation
  • •Eval on every change
  • •CI gate on faithfulness ≥ 0.85
  • •500 pairs for production confidence

Common Mistakes

  • ✕No eval dataset — tuning based on gut feel
  • ✕Only measuring answer quality, not retrieval quality
  • ✕Eval set too small (<50 pairs) — noisy metrics
  • ✕Not running eval in CI on pipeline changes
  • ✕Optimizing generation when retrieval is the bottleneck