AI Evaluations: How to Test Your RAG Pipeline Before Going Live

You built a RAG pipeline that answers questions from your company’s documents. It works great in demos. Then a customer asks a question about a product you discontinued last year, and the system confidently returns outdated pricing from a 2024 catalog.

This is why AI Evaluations exist. They’re automated tests for your RAG system — run a dataset of questions through your pipeline, measure the answers against expected results, and catch problems before your users do.

What AI Evaluations Measure

A RAG pipeline can fail in several ways. Good evaluations test for each:

flowchart TD
    QUERY["User Question"] --> RETRIEVE["Retrieval"]
    RETRIEVE --> GENERATE["Generation"]
    
    RETRIEVE --> E1["Retrieval Accuracy<br/>Did it find the right docs?"]
    GENERATE --> E2["Answer Correctness<br/>Is the answer right?"]
    GENERATE --> E3["Hallucination Rate<br/>Did it make things up?"]
    GENERATE --> E4["Completeness<br/>Did it answer fully?"]
    GENERATE --> E5["Citation Accuracy<br/>Do sources check out?"]
    
    style E1 fill:#3B82F6,color:#FAFAFA
    style E2 fill:#10B981,color:#FAFAFA
    style E3 fill:#EF4444,color:#FAFAFA
    style E4 fill:#F5A623,color:#0B1628
    style E5 fill:#8B5CF6,color:#FAFAFA

Metric	What It Measures	Why It Matters
Retrieval accuracy	Did the system find the right documents?	Wrong docs → wrong answers
Answer correctness	Is the generated answer factually correct?	Core quality metric
Hallucination rate	Did the model invent information not in the source docs?	Trust killer for enterprise use
Completeness	Did the answer address all parts of the question?	Partial answers frustrate users
Citation accuracy	Do the cited sources actually support the claims?	Auditability requirement

Building an Evaluation Dataset

The foundation of AI Evaluations is a test dataset — a set of question/expected-answer pairs that represent real usage:

[
  {
    "question": "What is the return policy for enterprise licenses?",
    "expected_answer": "Enterprise licenses have a 30-day full refund policy...",
    "expected_sources": ["policies/enterprise-license-agreement.md"],
    "category": "policy"
  },
  {
    "question": "How do I configure SSO with Azure AD?",
    "expected_answer": "Navigate to Admin > SSO > Add Provider > Azure AD...",
    "expected_sources": ["docs/sso-azure-setup.md"],
    "category": "technical"
  }
]

How many test cases? Start with 20-30 covering your most common query types. Expand to 100+ as you discover edge cases. Include:

• Happy path questions (things your docs clearly answer)
• Edge cases (questions that span multiple documents)
• Negatives (questions your docs DON’T answer — the system should say “I don’t know”)
• Temporal (questions about dates, versions, or things that change)

n8n AI Evaluations Workflow

n8n’s AI Evaluations feature lets you build this as a workflow:

flowchart LR
    DATA["Test Dataset<br/>(JSON/Sheet)"] --> LOOP["Loop Through<br/>Questions"]
    LOOP --> RAG["Run RAG<br/>Pipeline"]
    RAG --> SCORE["Score Answer<br/>vs Expected"]
    SCORE --> REPORT["Generate<br/>Report"]
    
    style DATA fill:#1E293B,color:#FAFAFA
    style RAG fill:#059669,color:#FAFAFA
    style SCORE fill:#F5A623,color:#0B1628
    style REPORT fill:#3B82F6,color:#FAFAFA

Step 1: Load Test Data

Read your evaluation dataset from a Google Sheet, JSON file, or database.

Step 2: Run Each Question Through Your RAG Pipeline

For each test case, send the question to your n8n + Ollama RAG workflow and capture the response.

Step 3: Score the Results

Compare the RAG response against the expected answer using:

• Exact match: Does the answer contain the key facts?
• Semantic similarity: Use embedding comparison to measure meaning overlap
• Source verification: Did the system retrieve the expected source documents?
• Hallucination check: Does the answer contain claims not present in the retrieved sources?

Step 4: Generate Report

Aggregate scores into a dashboard:

• Overall accuracy percentage
• Per-category breakdown (policy questions vs technical vs product)
• Failed cases with full context for debugging
• Trend over time (are you getting better or worse?)

Scoring Without a Judge LLM

You can evaluate RAG quality without needing GPT-4 or Claude as a judge. For local deployments, use these approaches:

Method	How It Works	Best For
Keyword matching	Check if key terms from expected answer appear in response	Simple factual questions
FAISS similarity	Embed both answers, compare cosine similarity	Semantic equivalence
Source overlap	Compare retrieved doc IDs against expected sources	Retrieval accuracy
Length ratio	Response length vs expected — too short = incomplete, too long = hallucination risk	Completeness proxy
Negative detection	For “don’t know” test cases, check if system correctly refuses	Safety

All of these run locally with Ollama — no cloud judge required.

When to Run Evaluations

Trigger	Why
After adding new documents	New docs might conflict with existing answers
After changing the model	Different models produce different quality
After changing retrieval settings	Chunk size, overlap, top-K all affect accuracy
Weekly scheduled	Catch drift from document updates
Before production deployment	Gate deployments on passing scores

Real Example: Our KB Evaluation

At VORLUX AI, we evaluate our own knowledge base (809 pages, 4,704 links) using a quality scoring system with 6 signals:

• Content depth (0-25): Is the article substantive?
• Crosslinks (0-20): Is it connected to related articles?
• Evidence backing (0-15): Does it cite sources?
• Confidence (5-15): How reliable is the content?
• Freshness (0-15): Is it recently updated?
• Search hits (0-10): Are users finding it useful?

Every page is automatically scored, and pages below threshold get flagged for improvement. This is RAG evaluation applied to the knowledge base itself — not just the answers it generates, but the quality of the underlying data.

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Want to deploy a tested RAG system? Schedule a free 15-minute assessment — we’ll help you build evaluation workflows that catch problems before your users do.

Related: n8n RAG Pipeline | n8n + MCP | Best Local LLMs | Quantization Guide

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Sources: n8n RAG Platform | n8n AI Agents | RAG Architecture Patterns | Enterprise RAG Guide

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Related reading

• AESIA: What Every Spanish Business Deploying AI Must Know in 2026
• Best Local LLM Models for Q2 2026: Practical Comparison for SMEs
• Cloud vs Local AI: Real Cost Analysis for Spanish SMEs in 2026

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