PuppyGraph Blog

Explore the most effective RAG techniques including Naïve, Hybrid, GraphRAG, Agentic, and Multi-Hop RAG. Learn how retrieval-augmented generation improves LLM accuracy, reasoning, and enterprise AI performance.

Explore how LLM knowledge graphs combine large language models with structured data to improve reasoning, accuracy, and explainability in AI systems.

Clustering architecture connects multiple nodes to act as one system for higher availability, better performance, and fault tolerance. Learn types, components, and how it works.

Evaluate data models, schema design, sharding and replication, global distribution, and developer tooling to choose the right platform for production graph workloads.

Learn what agent observability is, how to implement it with logs, metrics, and traces, and how to evaluate LLM agent outcomes across tools, retrieval, and multi-agent workflows. Query telemetry as a graph with PuppyGraph.

Compare GPT vs Neo4j graph database applications across data modeling, querying, accuracy, and use cases to choose the right approach.

Compare FalkorDB vs Neo4j across architecture, performance, scalability, and operational tradeoffs. Learn when to use each graph database.

Learn what an ETL graph is, how it visualizes extract-transform-load workflows, and how graph-based modeling improves data lineage, dependency tracking, and pipeline optimization.

Explore what a taxonomy graph is, how it works in organizing and linking concepts, key benefits, real-world use cases, and tools for building and managing taxonomy-based graph structures.

Compare MongoDB vs Neo4j across data modeling, queries, performance, and use cases to choose the right database for your application.

Compare Stardog vs Neo4j on RDF vs property graphs, SPARQL vs Cypher, reasoning, performance, and deployment. See which fits knowledge graphs, analytics, and enterprise needs.

A streaming database enables real-time data ingestion, processing, and querying. Learn how streaming databases work and where they are used.

A virtual graph lets you query connected data across sources without moving or replicating it. Learn how virtual graphs work and when to use them.

Learn what Graph AI is, how it works, and why it matters. Explore Graph Neural Networks, real-world use cases, key tools, and how Graph AI compares to traditional AI.

AI telemetry captures metrics, logs, traces, and signals from AI systems to enable monitoring, observability, and performance optimization.