Continuous Improvement: The Feedback Loop

The day you launch your Graph RAG system is only the beginning. Users will find "Gaps" in your knowledge. They will ask questions in ways your prompt didn't anticipate. Most developers treat these as bugs to be fixed manually. Professional AI engineers treat them as Signals for the Loop.

In this final lesson of Module 12, we will look at how to build a Feedback-Driven Graph. We will learn how to capture user "Thumbs Up/Down," how to identify "Missing Nodes" from failed searches, and how to use LLM-based "Failure Analysis" to automatically suggest schema improvements or new data sources.

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1. Capturing the Signal (Implicit vs. Explicit)

Explicit Feedback:

The user clicks a "Helpful" or "Not Helpful" button.

• RAG Tip: Always ask "Why?" for a thumbs down. Was the info missing? Was it too old? Was the logic wrong?

Implicit Feedback:

The user asks the same question three different ways.

• Signaling: This means the AI isn't finding the answer on the first try. The Cypher Generator is likely struggling with the user's phrasing.

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2. The "Gap Analysis" Report

Once a week, you should run a report of all "No Results Found" queries.

• Query: "How is Sudeep related to Project X?" -> Result: 0.
• Developer Action: Check the graph. Does Sudeep exist? Does Project X exist? If they both exist but aren't connected, you have a Missing Edge. This tells you that your "Extraction Pass" (Module 6) needs to be more aggressive.

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3. RLHF for Graphs (Reinforcement Learning from Human Feedback)

You can use user feedback to "Fine-tune" your Cypher Prompt.

1. Fail: LLM writes a bad query. User marks "Bad."
2. Fix: Developer manually writes the "Correct" query.
3. Learn: This pair (Question + Correct Cypher) is added as a Few-Shot Example (Module 8) in the system prompt.

The system will never make that specific mistake again.

graph LR
    U((User)) -->|Feedback| FR[Feedback Repository]
    FR -->|Analysis| AA[AI Analyst]
    AA -->|Suggest Edge| I[Ingestion Pipeline]
    AA -->|Suggest Prompt| P[Prompt Generator]
    I --> KG[(Knowledge Graph)]
    P --> AG[AI Agent]
    
    style FR fill:#f4b400,color:#fff
    style AA fill:#4285F4,color:#fff

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4. Implementation: Storing Feedback in the Graph

Why store feedback in a separate SQL DB? Store it in the Graph!

MATCH (u:User {id: 'session_123'}), (a:Answer {id: 'ans_456'})
CREATE (u)-[:GAVE_FEEDBACK {score: -1, reason: 'Outdated info'}]->(a)

Pro Tip: Your AI agent can now "Read its own feedback" to avoid making the same mistakes in the current session.

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5. Summary and Exercises

Continuous Improvement is what separates "Demos" from "Products."

• Implicit and Explicit feedback provide the data for refinement.
• No-result queries identify gaps in the knowledge base.
• Corrected Cypher should be fed back into the few-shot prompts.
• Graph-based feedback allows for session-aware self-correction.

Exercises

1. Feedback Design: Design a "Feedback Form" for a corporate RAG bot. What are the 3 most important questions to ask a user who is unhappy with an answer?
2. Gap Identification: You see 10 users asking about "The New CEO." Your graph has a node for "The CEO" but the name is still the old one. Is this a Schema problem or a Freshness problem?
3. The Loop Logic: If a user marks an answer as "Excellent," should you automatically add it to your "Few-Shot" examples? What is the risk of doing this without a human review?

Congratulations! You have completed Module 12: Evaluating Graph RAG Systems. You now have the metrics and the loops to ensure your AI gets smarter every day.

In Module 13: The Graph RAG Production Stack, we will look at how to deploy and secure this entire factory.