Neo4j

Field Guide: Production-Grade RAG Architectures

This technical guide maps Retrieval-Augmented Generation (RAG) as a design space and describes practical production patterns and failure modes. It defines three evolutionary paradigms — Naive RAG, Advanced RAG (pre/post-retrieval optimizations), and Modular RAG (composable pipelines) — and catalogs eight architectural patterns: Standard (Dense), Hybrid, GraphRAG, Corrective RAG (CRAG), Self-RAG, Adaptive RAG, Agentic/Multi-Agent RAG, and Multi-Modal RAG. The article explains common production failures (chunking, semantic drift, multi-hop needs, static top-k, hallucination) and recommends incremental upgrades — notably hybrid dense+sparse search with re-ranking — and routing by query complexity. It includes runnable Python examples for hybrid retrieval + re-ranking and a simple CRAG-style relevance gate, plus an architectural decision matrix comparing complexity, latency, cost, and best use cases.

AI Agents Revive Ontologies and the Semantic Web

AI engineers and researchers are revisiting ontologies and Semantic Web technologies to provide logical guardrails for agentic systems built on large language models. At the AI Engineer World’s Fair, UC Berkeley professor Frank Coyle and Neo4j CEO Emil Eifrem argued that ontologies—described as "data as graphs"—can validate reasoning, enforce rules (e.g., OWL axioms), and enable a shared semantic layer for thinner, scalable agents. Practitioners like Kingsley Idehen (OpenLink Software) are combining RDF memory and Semantic Web stacks with agents, while developers suggest agents could maintain and update ontologies during operation. The article frames this as a 2026 revival of software engineering discipline focused on quality control for loop engineering in agent systems.

Learn Neo4j by Modeling 75 Years of F1

A technical tutorial demonstrating how to model 75 years of Formula 1 data in Neo4j. The author explains the labeled property graph model, shows how to import CSVs (drivers, constructors, races, results) from a Kaggle dataset, create uniqueness constraints, aggregate race results into per-driver-per-team-per-season DROVE_FOR relationships, derive TEAMMATE_OF relationships, and run variable-length shortest-path Cypher queries (e.g., connecting Max Verstappen to Juan Manuel Fangio). The post highlights graph design decisions, data-cleaning tips, and how the same graph techniques transfer to other domains.