An architecture-focused comparison of Graph RAG and vector RAG: chunking, storage, retrieval behavior, trade-offs, and hybrid search patterns.
Graph RAG vs Vector RAG: When to Use Each
Retrieval-Augmented Generation (RAG) helps LLMs use external knowledge more reliably. In practice, two patterns show up often: Vector RAG and Graph RAG.
Both try to solve the same problem: bring relevant context to the model. They just do it with different data models.
Vector RAG: similarity-based retrieval
Graph RAG: relationship-based retrieval
Beyond Retrieval | Pinecone
Enterprise AI agents fail because they forget
How to use retrieval augmented generation with ChromaDB and Mistral – Replicate blog
Context Retrieval for AI Agents: What It Is & Why It Matters
Your AI agent is rediscovering 85% of its context every run. Here's the architecture fix (+ Contract Spec, Failure Triage, and Stack ADR)
Context architecture is replacing RAG in AI
iX-Workshop: KI für eigene Daten: RAG in der Praxis mit LangChain.js
What is a context engine?
Most teams building LLM applications start with RAG for a good reason. It is practical, easy to...
How to build retrieval-augmented generation that actually cites its sources — without a vector database subscription.
Introduction Here is a scenario many RAG builders know well, you wire up a pipeline, load...
The vector database category is undergoing a shift in response to the needs of agentic AI.
Ask five AI engineers which vector database to use for your RAG system. You'll get five different...
Explore the limitations of foundation models and how retrieval-augmented generation (RAG) can address these limitations so chat,…
