A comprehensive, professional guide explaining the differences, strengths, and best practices of Retrieval-Augmented Generation (RAG) and Fine-Tuning for LLMs, including workflows, comparisons, decision frameworks, and real-world hybrid AI use cases.

Hybrid AI is the future of explainable intelligence. This article explores how combining vector search, knowledge graphs, and retrieval-augmented generation (RAG) creates AI systems that can reason, cite, and explain their answers with insights learned from building a real Graph-Powered RAG Engine.

⚡ Production-ready .NET Standard 2.1 RAG library with 🤖 multi-AI provider support, 🏢 enterprise vector storage, 📄 intelligent document processing, and 🗄️ multi-database query coordination. 🌍 Cross-platform compatible.

GA + LLM hybrid framework for structured text generation and task optimization.

A modular toolkit for designing, analyzing, and validating hybrid AI systems using Boxology visual patterns.

A Hybrid AI Agent for Quantitative Football Prediction Analysis

Quantum Natural Language Processing (QNLP) using Quantum LSTM (QLSTM) architectures for advanced text classification tasks. This project demonstrates how quantum-inspired LSTM networks can be applied to natural language understanding and classification using Qiskit/PennyLane.

Гибридная модель ИИ для автошахмат, сочетающая формальные эвристики и адаптивное поведение.

A hybrid AI model for predicting failures in water distribution systems using Adaptive Neuro-Fuzzy Inference System (ANFIS). The model integrates Genetic Algorithms (GA) and Ant Colony Optimization (ACO) to improve the accuracy of accident prediction.

Hybrid AI orchestration stack combining local LLMs (Ollama), vector search (Qdrant), and Azure AI Foundry for scalable RAG, Agentic AI, and Vision. Built with .NET 8 and Python.

SymRAG adaptively routes queries through neuro-symbolic, neural, or hybrid paths based on complexity and system load, ensuring efficient and accurate RAG for diverse QA tasks.

HAL, the Hybrid Artificial-Intelligence Layer, is the future of artificial intelligence. It's a layered approach that combines different AI techniques to create a more complex and adaptable system. HAL's layers work together to gather data from the environment, interpret it, learn from it, and make informed decisions based on that knowledge.

textual entailment experiments on Mednli dataset.

Rust LLM-driven deterministic graph core (v0 prototype)

Hybrid GNN-SNN AI model for early Parkinson's disease detection using spiking neurons and graph learning

🧠 Linguaggio Cognitivo Ibrido per Agenti AI Trasparenti | Hybrid AI Language combining symbolic reasoning with LLMs

An LLM Agent that combines Symbolic Reasoning (RAG) with a Code Interpreter to answer specialized scientific research questions.

Hybrid AI + Kalman Filter-based Satellite Attitude Determination and Control System (ADCS) — Python simulation of a 3-axis CubeSat control system combining Extended Kalman Filtering and Neural Network-based adaptive control for high-precision spacecraft attitude stability.

Hybrid classical-quantum system for gas leak detection and drone path planning using synthetic GPLA-12 data, classical ML, and quantum-inspired optimization.