Designing Hybrid AI Systems | honardoust.codes

NOTE-005 / question

Central question

How can vector search, knowledge graphs, and RAG work together to build more explainable AI systems?

NOTE-005 / key ideas

Hybrid architecture

The essay argues that neural and symbolic methods can complement each other in practical AI systems.

Graph + vector retrieval

Vector search provides flexible retrieval, while graph structure adds relationships, paths, and interpretability.

Project connection

The article links the design ideas to a real Graph-RAG prototype using FAISS, graph reasoning, and RAG pipelines.

NOTE-005 / skills demonstrated

AI architecture

Shows system-level reasoning about retrieval, graphs, and LLM application design.

Explainable AI

Connects technical architecture to transparency, reasoning paths, and citations.

Technical writing

Turns project experience into a reusable design lesson for hybrid AI systems.

NOTE-005 / related work

Graph-RAG Engine

NOTE-005 / source

This page summarizes and positions the essay inside the honardoust.codes lab index. The full original essay is kept in its GitHub repository.