G-Eval for Graph-Grounded Evaluation: The Judge Agent

In a system with millions of facts, a human cannot grade every AI answer. We need an automated Judge. G-Eval is a framework that uses an LLM (typically a highly capable one like GPT-4o or Gemini Pro) to evaluate the output of a "Student LLM" based on a set of criteria.

In this lesson, we will look at how to build a Graph-Aware Judge. We will teach the Judge how to read a list of graph triplets and compare them to the Student's answer. We will learn how to create a "Grading Rubric" for Relationship Accuracy and Logical Coherence, and how to handle the inevitable "Subjectivity" of AI-judging.

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1. The G-Eval Workflow for Graphs

1. Input: User Question + Student Answer + Retrieved Graph Subgraph.
2. Scoring Rubric: The Judge is given a set of weights:
   • 5 points: Answer is fully grounded in the subgraph.
   • 3 points: Answer is partially grounded but adds "Fluff."
   • 0 points: Answer contains a relationship that contradicts the graph.
3. The Verdict: The Judge provides a score and—crucially—a Justification.

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2. Why "Graph-Grounded" is Easier to Judge

In Vector RAG, the context is often a "Messy Paragraph." The Judge has to read between the lines. In Graph RAG, the context is a List of Triplets.

• Graph Context: (Sudeep)-[:ROLE]->(Lead)
• Answer: "Sudeep is the CEO."
• Judge Result: "FAIL. Explicit contradiction found. Expected ROLE:Lead, found ROLE:CEO."

Because graphs are structured, the Judge can reach a "Correct" verdict much faster and more reliably.

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3. The "Chain-of-Thought" (CoT) Judge

Avoid asking the Judge for just a number. Always ask for its Reasoning. "Explain why you gave a 3/5 for the Path-Accuracy."

When the Judge explains its reasoning, you can use that data to fix your Ingestion Pipeline or your Graph Schema.

graph TD
    S[Student LLM] -->|Answer| J[Judge LLM]
    G[(Graph Subgraph)] -->|Truth| J
    R[Rubric] --> J
    J -->|Score + Reason| D[Developer Dashboard]
    
    style J fill:#f4b400,color:#fff
    style G fill:#4285F4,color:#fff

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4. Implementation: A G-Eval Prompt in Python

JUDGE_PROMPT = """
You are a 'Graph Integrity Auditor'. 
TRUTH_DATA (Graph Triplets):
{retrieved_facts}

STUDENT_ANSWER:
{answer}

GOAL: Rate the 'Relationship Accuracy' from 1-5.
- If the student states a relationship NOT present in the TRUTH_DATA, you MUST score it below 3.
- If the student correctly traces a 2-hop path, score it 5.

REASONING:
SCORE:
"""

# result = judge_llm.invoke(JUDGE_PROMPT.format(...))

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5. Summary and Exercises

G-Eval provides the "Scientific Rigor" needed for enterprise deployments.

• Judge Agents automate the "Ground Truth" check.
• Structured Context (Triplets) makes judging faster and more accurate than text-chunks.
• Detailed Reasoning from the judge helps developers identify which part of the graph is "Confusing" the AI.
• Rubrics ensure consistency across thousands of test cases.

Exercises

1. Rubric Design: Write a 5-point rubric for evaluating "How well an agent handles conflicting facts from two different graph sources."
2. The "Bias" Check: If the Judge is GPT-4o and the Student is also GPT-4o, is there a risk of "Self-Grading Bias"? How would you mitigate this? (Hint: Use a different model class or a more rigid schema-check).
3. Visualization: Draw a flow chart where a "Fail" from the Judge automatically triggers a "Retraining" or "Re-ingestion" event.

In the next lesson, we will build the actual data for our judge: Building a Test Suite of Complex Graph Questions.