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AI Architect: GraphRAG & Defensible Data Moats

Go beyond vector search with knowledge graphs. Learn to extract entities, build Neo4j graphs, write LLM-to-Cypher translators, and connect live streaming data pipelines — creating a retrieval system that compounds in value over time.

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5 weeks

What You'll Learn

Identify the failure modes of pure vector RAG and when graphs are necessary
Extract entities, disambiguate them, and summarize them into a knowledge graph
Query Neo4j using Cypher and translate natural language queries automatically
Build hybrid retrieval combining vector search and graph traversal
Connect live streaming data to keep the graph continuously updated

Course Content

W1
Week 1: Graph Knowledge Representation
Model relationships your vector store can't express.
1
Limitations of Vector RAG
Analyze the retrieval failure modes that emerge from pure semantic search — multi-hop reasoning, entity resolution, and recency blind spots.
2
Knowledge Graph Topologies
Survey property graph, RDF, and hypergraph topologies and select the right structure for different knowledge domains.
3
Automated Entity Extraction
Use LLMs and NER models to extract entities, relationships, and attributes from unstructured documents at scale.
4
Entity Disambiguation
Resolve co-references and merge duplicate entity mentions into canonical nodes using embedding similarity and rule-based heuristics.
5
Graph Summarization
Generate community summaries and cluster-level abstractions over graph regions to enable global context retrieval.
Weekly Win
Populated Entity Graph
Extract entities and relationships from a 50-document corpus and load them into a structured graph representation.
W2
Week 2: Neo4j & Graph Querying
Store, query, and secure your knowledge graph in production.
1
Introduction to Neo4j
Stand up a Neo4j instance, understand the property graph model, and navigate the browser UI and Python driver.
2
Cypher Query Language
Write MATCH, CREATE, MERGE, and aggregation queries in Cypher to traverse and update your knowledge graph.
3
LLM-to-Cypher Translation
Build a chain that converts natural language questions into valid Cypher queries using few-shot prompting and schema injection.
4
Graph Write Operations
Implement idempotent MERGE operations and constraint-backed upserts to keep the graph consistent as new data arrives.
5
Security and Graph RBAC
Configure role-based access control in Neo4j to restrict node and relationship visibility by user role and data classification.
Weekly Win
Natural Language Graph Query Interface
A Python function that accepts plain English questions, translates them to Cypher, and returns structured results from Neo4j.
W3
Week 3: Hybrid Retrieval Architecture
Combine graph traversal and vector similarity for retrieval that neither can achieve alone.
1
The Hybrid Retrieval Paradigm
Fuse vector similarity search with graph traversal at query time to retrieve semantically similar and relationally connected context together.
2
Multi-Hop Traversal Logic
Implement graph traversal queries that follow relationship chains to surface indirect connections invisible to single-hop retrieval.
3
Retrieval Evaluation Metrics
Measure retrieval quality using recall@k, precision@k, and NDCG across both vector and graph retrieval paths.
4
Graph-Anchored Prompting
Inject graph context — entities, relationships, and community summaries — into LLM prompts to ground responses in verified facts.
5
Traceable Citations
Surface the source nodes and relationships behind every claim so users and auditors can verify the provenance of each answer.
Weekly Win
Hybrid Retriever with Citations
A retrieval pipeline that combines vector search and two-hop graph traversal, with every answer traceable to a source node.
W4
Week 4: Streaming Data Pipelines
Keep your knowledge graph alive as the world changes.
1
The Ingestion Bottleneck
Identify where batch ingestion creates data freshness gaps and why streaming architectures are necessary for live knowledge bases.
2
Change Data Capture (CDC)
Use CDC to capture database row-level changes at the transaction log level and stream them into your graph update pipeline.
3
Kafka Event Buffering
Buffer incoming entity events in Kafka topics with appropriate retention, partitioning, and consumer group configuration.
4
Kafka-to-Neo4j Connectors
Deploy the Neo4j Kafka Connector to consume events and apply graph mutations in real time with at-least-once delivery guarantees.
5
Handling Streaming Failures
Implement dead-letter topics, idempotent consumers, and schema validation to handle malformed events without corrupting the graph.
Weekly Win
Live-Updating Knowledge Graph
A pipeline where new documents trigger Kafka events that automatically extract entities and update the Neo4j graph within seconds.
W5
Week 5: Capstone — Live GraphRAG System
Deploy a knowledge graph that retrieves, traces, and learns continuously.
1
Capstone: Schema Definition
Define node labels, relationship types, property constraints, and indexes for your production knowledge graph schema.
2
Capstone: Initial Batch Extraction
Run the full entity extraction pipeline over the historical document corpus and load the resulting graph into Neo4j.
3
Capstone: Live Stream Integration
Connect the Kafka-to-Neo4j pipeline and verify that new document ingestion triggers automatic graph updates within SLA.
4
Capstone: Hybrid Retriever Construction
Wire the vector index and graph traversal into a unified retrieval API with citation metadata in every response.
5
Capstone: UX and Final Verification
Build a query interface over the retriever, run end-to-end retrieval quality tests, and deploy the system to a cloud environment.
Weekly Win
Deployed Live GraphRAG System
A production GraphRAG system with live Kafka updates, hybrid retrieval, traceable citations, and a user-facing query interface.

Prerequisites

Python and SQL proficiency
Basic vector RAG experience
Familiarity with LLM APIs

Hands-on Project

Build a GraphRAG system over a live document corpus with Kafka-powered updates, hybrid retrieval, traceable citations, and a query interface.

📚
Advanced Level
Course Price
₹14,999
India
$249
International · One-time payment
Next cohort starts Mar 30
Duration5 weeks
LevelAdvanced
FormatCohort-based
Modules5

What's included:

Live cohort sessions
Hands-on projects
Certificate of completion
Lifetime access
Career support

Part of Learning Track

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AI Architect
7 courses in track