Implicit vs Explicit Reasoning in Graph RAG

When we ask an AI a complex question, we are asking it to Reason. In traditional Vector RAG, the reasoning is almost entirely Implicit: the AI looks at a pile of text and tries to "Intuit" the connections. In Graph RAG, we have a choice. We can either provide the "Seeds" and let the AI deduce the link, or we can provide the Explicit Path from the database.

In this lesson, we will compare the two strategies. We will learn why Explicit Reasoning (showing the path) is the key to enterprise reliability and why Implicit Reasoning (leaving it to the LLM) is the #1 cause of "Confident Hallucinations" in RAG systems.

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1. Implicit Reasoning: The Semantic Guess

The Process:

1. Query: "Does Sudeep know the Project Titan lead?"
2. Vector search returns chunks about Sudeep and Jane (the lead).
3. LLM looks at the two chunks. They mention the same office.
4. Implicit Conclusion: "Yes, they probably know each other because they work in the same office."

The Risk: This is an Inference, not a Fact. If they work in the same office but have never met, the AI is lying.

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2. Explicit Reasoning: The Topological Fact

The Process:

1. Query: Same.
2. Graph search finds the shortest path: (Sudeep)-[:WORKS_ON]->(ProjectX)<-[:WORKS_ON]-(Jane).
3. Context sent to LLM: "Sudeep and Jane both work on Project X."
4. Explicit Conclusion: "Yes, they are connected through their work on Project X."

The Benefit: The AI's logic is Traceable. The answer is grounded in a physical edge in your database.

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3. When to use Each

• Implicit: For "Vibe-based" or creative questions ("What is the general feeling of the team?").
• Explicit: For "Logic-based" or high-stakes questions ("Who approved the payment?").

graph TD
    subgraph "Implicit (Probability)"
    V[Vector Chunks] -->|LLM Intuition| A1[Maybe/Guess]
    end
    
    subgraph "Explicit (Logic)"
    G[Graph Path] -->|Logical Proof| A2[Fact-Based Answer]
    end
    
    style G fill:#34A853,color:#fff
    style V fill:#9e9e9e,color:#fff

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4. Summary and Exercises

Reasoning is the "Engine" that drives the answer.

• Implicit reasoning relies on the model's internal weights and training data.
• Explicit reasoning relies on your company's Knowledge Graph.
• Path-grounding is the only way to eliminate "Logic Hallucinations."
• Transparency: If a user asks "How do you know?", an Explicit system can show the graph path; an Implicit system can't.

Exercises

1. Reasoning Audit: Look at an AI response. Does it use words like "Therefore," "Suggests," or "Likely"? These are signs of Implicit reasoning.
2. Conversion Task: Take the implicit guess "They both live in London, so they probably know each other." What graph relationship would you need to turn this into an Explicit fact?
3. Visualization: Draw a solid line (Explicit) and a dotted line (Implicit) between two points. Which one feels more "Reliable"?

In the next lesson, we will look at the "Grammar of Movement": Teaching models to 'Walk' the Path.