AI-Native Knowledge Graphs - Hands-On Analysis & Design

Eleven runnable lessons on Neo4j · Pydantic · Groq Two running cases: AlphaRe (reinsurance) and VinTech (supply chain)

By: VU TRI CONG MOBILUCK - CODE247.AI

Narrated by: Virtual Voice

Your data is correct. Your AI's answers are wrong. Here is why - and the fix.

You connect a language model to a clean, normalised database and something strange happens: the facts are right, but the answers are wrong in a business sense. The model rejects a claim that a pending renewal should have saved, quotes a rule that expired last year, and cannot say which factory stops when a port closes. The cause is simple: we hand the model isolated rows and expect it to reconstruct the knowledge on its own. It cannot.

AI-Native Knowledge Graphs is a hands-on course in eleven runnable lessons that turns flat data into connected, governed, AI-ready knowledge. Every lesson is a working laboratory with real code, a real database (PostgreSQL, then Neo4j) and real LLM calls. Every block of code is followed by the question that matters: why is it written this way?

After reading and practising you will be able to:

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• Prove with an A/B experiment why connected context beats flat data
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• Model a domain as a graph and write Cypher that follows multi-hop reasoning paths
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• Protect the graph with Pydantic data contracts and Neo4j constraints
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• Version rules without overwriting them, time-travel to the rule in force, and route decisions by confidence
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• Extend the graph by inheritance and enrich nodes with summaries, keywords and embeddings for hybrid search
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• Choose a design method with a decision tree and run a seven-step process that ends with golden queries
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• Design multi-layer ontologies with a Shared Kernel, a relationship pattern library and cardinality guards
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• Govern a knowledge lifecycle - Draft, Reviewed, Published, Deprecated - and detect drift with a freshness score
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• Feed a graph into RAG with structure-aware chunking and parent-document retrieval
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• Measure graph quality with coverage, density and queryability, and gate deployment with a CI/CD loop
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Two fictitious companies carry the examples - a reinsurer and a manufacturer with a global supply chain - so every rule is stated and every result is verifiable. Each lesson opens with objectives and a summary and closes with an eight-question self-test whose answers are on a separate page. The complete, runnable code for all eleven lessons is included.

Written for software engineers, data engineers and solution architects who already know SQL and Python and are being asked to make an AI assistant answer business questions reliably. No prior graph-database experience required.

MOBILUCK · code247.ai