Machine Learning Mastery offers developers resources and tutorials on machine learning algorithms, techniques, and applications. Developers can learn about supervised and unsupervised learning methods, deep learning frameworks, and practical machine learning projects. Additionally, the blog covers topics such as data preprocessing, model evaluation, and hyperparameter tuning, providing  insights for both beginners and experienced practitioners in the field of machine learning.

Machine Learning Mastery

A tutorial on building a deterministic three-tiered Graph-RAG system that combines a lightweight QuadStore knowledge graph with a ChromaDB vector database to reduce factual hallucinations. The architecture uses three priority layers: absolute graph facts (SPOC quads), statistical graph data, and vector documents as fallback. Instead of complex algorithmic routing, all three sources are queried simultaneously and a prompt-enforced hierarchy instructs the language model to resolve conflicts deterministically. The system is demonstrated using fabricated NBA data with a local Llama 3.2 3B model via Ollama, showing how Priority 1 facts override conflicting lower-tier data.

Beyond Vector Search: Building a Deterministic 3-Tiered Graph-RAG System

Architecture Overview: The 3-Tiered Hierarchy

Step 1: Building a Lightweight QuadStore (The Graph)

Step 3: Entity Extraction & Global Retrieval

Step 4: Prompt-Enforced Conflict Resolution