RAG Evaluation: The Complete Guide

Retrieval-Augmented Generation (RAG) systems are powerful—but only if your retrieval actually works.

In this guide, I'll show you how to evaluate every component of your RAG system, from embedding quality to answer grounding.

The RAG Evaluation Challenge

Traditional LLM evaluation isn't enough for RAG because you need to test TWO things:

1. Retrieval Quality: Did you fetch the right documents?
2. Generation Quality: Did the LLM use those documents correctly?

If either fails, your system fails.

The RAGAS Framework

RAGAS (Retrieval-Augmented Generation Assessment) is the industry-standard framework. It measures 5 key metrics:

1. Context Precision

What it measures: Are the retrieved documents actually relevant?

Formula: (# relevant documents retrieved) / (# total documents retrieved)

Target: > 0.8

Example:

2. Context Recall

What it measures: Did you retrieve ALL relevant documents?

Formula: (# relevant documents retrieved) / (# relevant documents that exist)

Target: > 0.9

3. Faithfulness (Answer Grounding)

What it measures: Is the answer supported by retrieved context?

Formula: (# claims supported by context) / (# claims in answer)

Target: > 0.95

Example of LOW faithfulness:

4. Answer Relevance

What it measures: Does the answer address the question?

Target: > 0.9

5. Answer Correctness

What it measures: Is the answer factually accurate?

Requires: Ground truth answers for comparison

Target: > 0.8

How to Implement RAG Evaluation

Step 1: Install RAGAS

Step 2: Prepare Test Dataset

You need:

• Questions: 100+ real user queries
• Ground truth contexts: Which docs SHOULD be retrieved
• Ground truth answers: What the correct answer is

Step 3: Run Evaluation

7 Critical Tests for RAG Systems

Test 1: Retrieval Accuracy

What: Do you fetch the right documents?

How: Compare retrieved docs to ground truth

Pass criteria: Precision > 0.8, Recall > 0.9

Test 2: Answer Grounding

What: Are answers supported by context?

How: RAGAS faithfulness metric

Pass criteria: > 0.95

Test 3: Uncertainty Handling

What: Does system say "I don't know" when appropriate?

Test case:

Test 4: Multi-Hop Reasoning

What: Can system combine info from multiple docs?

Example:

Test 5: Contradictory Information

What: How does system handle conflicting docs?

Example:

• Doc 1 (outdated): "Returns within 14 days"
• Doc 2 (current): "Returns within 30 days"

Expected: Use most recent/authoritative source

Test 6: Security (Indirect Prompt Injection)

What: Malicious instructions hidden in documents

Attack scenario:

Test 7: Performance at Scale

• Retrieval latency < 500ms
• End-to-end response < 3 seconds
• No degradation with 10K+ documents

Optimization Strategies

If Context Precision is Low (<0.7)

• Problem: Retrieving irrelevant documents
• Fixes:
  • Improve embeddings (use domain-specific model)
  • Add metadata filters
  • Rerank results (use cross-encoder)
  • Tune retrieval parameters (top_k, similarity threshold)

If Context Recall is Low (<0.8)

• Problem: Missing relevant documents
• Fixes:
  • Increase top_k (fetch more docs)
  • Improve chunking strategy
  • Add query expansion
  • Use hybrid search (keyword + semantic)

If Faithfulness is Low (<0.9)

• Problem: LLM hallucinating despite good context
• Fixes:
  • Strengthen prompt: "Answer ONLY based on provided context"
  • Lower temperature (0.0-0.2)
  • Add citation requirements
  • Use output validation

Tools for RAG Evaluation

Your RAG Evaluation Checklist

Common Mistakes

• ❌ Testing only retrieval OR generation → Test both
• ❌ Using synthetic test data → Use real user queries
• ❌ No ground truth → Can't measure accuracy
• ❌ Ignoring edge cases → Test ambiguity, multi-hop, contradictions
• ❌ One-time evaluation → Set up continuous monitoring

Conclusion

RAG systems are only as good as their retrieval. Use RAGAS metrics to measure and optimize every component.

Download our complete RAG audit template: