The 'Graph-Grounded' Answer: Verifying the Logic

You have retrieved the facts. You have reasoned through the paths. The AI has given an answer. But can you trust it? LLMs are creative by nature. Even with the best graph context, they can still "Hallucinate" a bridge or a date. To build a Mission-Critical system, you need a final Verification Step.

In this final lesson of Module 12, we will look at Grounded Verification. We will learn how to build a "Self-Correction" loop where a second AI auditor compares the final answer against the raw triplets. We will see how to identify "Un-grounded" sentences and how to force the AI to provide a "Citations" list that links every claim to a specific Node ID.

1. The Verification Workflow

Generate Answer: The "Student" LLM writes the response.
Extract Claims: The "Verifier" LLM breaks the answer into individual atomic facts.
Traceability Check: For every claim, the system asks: "Is there an edge in our graph result that matches this claim?"
Rewrite/Reject: If a claim is not grounded, the AI is forced to rewrite the answer or mark it as "Uncertain."

2. Generating with Citations

Don't just ask for an answer. Ask for IDs.

System Prompt: "For every fact you state, append the Node ID in brackets, e.g., 'Sudeep [N-101] works at Google [N-552]'."

This forces the LLM to "Lookup" the fact in the provided context window. If it tries to invent a fact, it won't have a matching ID to attach to it. This is the Strongest Defense against Hallucinations.

3. The "Uncertainty" Threshold

If the verification fails for more than 20% of the claims, the system should return a Failure Message: "I can see the relevant entities, but I cannot find a verified logical connection between them to answer your question accurately."

This "Polite Refusal" is a sign of a professional, high-reliability AI system.

graph TD
    A[AI Answer] --> V[Claim Extractor]
    V -->|Fact 1 / Fact 2| G[Path Verifier]
    C[(Graph Subgraph)] --> G
    G -->|VERIFIED| F[Final Answer + Citations]
    G -->|UNVERIFIED| R[Rewrite/Refuse]
    
    style F fill:#34A853,color:#fff
    style R fill:#f44336,color:#fff

4. Implementation: A Verification Prompt

EVIDENCE:
(Sudeep)-[:WORKS_ON]->(ProjectX:Deadline=June)

ANSWER TO VERIFY:
"Sudeep is the project manager for Project X which ends in July."

VERIFICATION TASK:
1. Is 'Sudeep works on Project X' grounded? YES.
2. Is 'Sudeep is Project Manager' grounded? NO (Role not in graph).
3. Is 'Ends in July' grounded? NO (Graph says June).

VERDICT: Fails groundedness check.

5. Summary and Exercises

Verification is the "Safety Valve" of the Graph RAG engine.

Claim Extraction identifies what the AI is actually promising.
Traceability proves the source of every promise.
Citations (using IDs) ground the AI in the provided context window.
Self-Correction allows the system to fix its own logic errors before the user sees them.

Exercises

Verification Check: An AI says "Project A uses Python." The graph says (Project A)-[:USES]->(Script_X) and (Script_X)-[:WRITTEN_IN]->(Python). Is the AI's claim grounded? (Hint: Yes, it's a 2-hop grounded path!).
Citation Strategy: Why is using [Node ID] better than using [Document Name] for citations in a Graph RAG system?
Visualization: Draw a "Green Checkmark" next to verified claims and a "Red X" next to hallucinated ones in a sample answer.

Congratulations! You have completed Module 12: Reasoning and Multi-Hop Inference. You have moved from simple retrieval to sophisticated logical proof.

In Module 17: Graph RAG System Architecture, we will look at how to put all these pieces together into a coherent software system.