Constraint Injection in Multi-Step Inference

An AI model's reasoning is often "Wild." If you ask it for the "Shortest path to the goal," it might suggest a path through a "Forbidden" node (e.g., a high-security server) or a "Impossible" path (e.g., a project that was cancelled 3 years ago). To build a Professional Reasoning Agent, you must inject Constraints at every step of the multiple-hop process.

In this lesson, we will look at how to build Constraint-Aware Prompts. We will learn how to enforce Temporal Constraints ("Don't look at anything before 2024"), Security Constraints ("Only look at 'Public' nodes"), and Logic Constraints ("Only follow 'Active' relationships"). We will see how these constraints prevent the AI from giving "Technically Correct but Practically Useless" answers.

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1. Types of Reasoning Constraints

A. Temporal Constraints (The "Freshness" Fence)

"Find the current CEO."

• If you don't inject a WHERE r.current = true constraint, the AI might reason its way to the CEO from 2010.

B. Path Constraints (The "Bridge" Fence)

"Find the connection between Sudeep and the Code."

• Constraint: "The connection MUST go through a Peer Review relationship."
• This prevents the AI from finding a "Friendship" link and using that as evidence for professional authorization.

C. Resource Constraints (The "Efficiency" Fence)

"Find only the top 3 most relevant results."

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2. In-Prompt Constraint Enforcement

Instead of just putting constraints in the Cypher query (Module 8), you must also put them in the System Prompt for the Reasoner.

DURING YOUR REASONING:
1. Ignore any fact with a date before 2024-01-01.
2. If a project is marked 'DEPRECATED', treat its neighbors as 'INVALID'.
3. Do not assume 'KNOWS' implies 'AUTHORIZATION'.

This teaches the AI the "Rules of the World" so it doesn't build a reasoning chain on shaky ground.

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3. The "Negative Search" Pattern

Sometimes the best constraint is telling the AI what NOT to find. "Search for the path between A and B, but EXCLUDE any node with the label 'Restricted'."

This is a powerful way to ensure that the AI's "Brain" never even sees the sensitive data that could lead to an unauthorized inference.

graph LR
    subgraph "Reasoning Path"
    A[Start] --> B[Step 1]
    B --> C[Step 2]
    C -->|Blocked by Constraint| D[Restricted Fact]
    C -->|Allowed| E[Public Fact]
    end
    
    style D fill:#f44336,color:#fff
    style E fill:#34A853,color:#fff

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4. Implementation: A Constraint-Aware System Prompt

You are a 'Policy-First' reasoning agent.
RULE: Every step in your chain must involve an 'ACTIVE' project.

EVIDENCE:
- Sudeep [MEMBER_OF] Team Alpha (Active)
- Team Alpha [OWNS] Server 1 (Deactivated)

REASONING:
1. Sudeep is in Team Alpha (Valid).
2. Team Alpha owns Server 1, but Server 1 is Deactivated. 
3. STOP. Per rule, I cannot use Server 1 for this reasoning.

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5. Summary and Exercises

Constraint injection is the "Guardrail" for AI logic.

• Temporal constraints stop the AI from living in the past.
• Security constraints protect the integrity of the data access layer.
• Logic constraints ensure that the AI follows "Business Reality" rather than "Graph Probability."
• Explicit Rules in the prompt are the most reliable way to enforce these constraints across multiple hops.

Exercises

1. Constraint Writing: Write a constraint that prevents an AI from using "Slack Gossip" as evidence for a "Financial Approval."
2. The "Strict" Auditor: What happens if the AI finds a 10-hop path but it violates one constraint at Hop #9? Should it discard the whole path?
3. Visualization: Draw a graph with "Active" (Green) and "Inactive" (Red) nodes. Trace a path from Start to End using only Green nodes.

In the next lesson, we will look at truth-checking: Detecting Path Contradictions in Reasoners.