AI/ML Articles

A pragmatic analysis of the costs, performance, and complexity of running local LLMs versus cloud providers, and why we adopted a hybrid architecture.

How we use Model Context Protocol to create a closed-loop design system where agents verify implementation against Figma specs automatically.

How to extract entities (NER) from documents using LLMs and model them effectively in FalkorDB for knowledge graphs.

Techniques for rewriting user queries (HyDE, Expansion) and reranking results to boost retrieval accuracy in Retrieval-Augmented Generation.

Implement effective text chunking strategies for RAG pipelines with semantic boundaries, overlap, and metadata preservation.

Extract named entities from documents using spaCy NLP service for building knowledge graphs and improving search relevance.

A production blueprint for combining graph traversal and vector search to improve recall and precision in medical document retrieval.

How BlueRobin introduced a GraphRAG routing layer with health-aware fallback to keep agentic retrieval reliable in production.

What happens when agent graph node names collide with state keys, and how to design LangGraph flows that remain safe under change.

How BlueRobin evolved entity extraction by combining deterministic and LLM-based providers with confidence-aware ensembling.

How BlueRobin combines multiple retrieval strategies and uses reciprocal rank fusion to produce more stable, high-quality contexts for LLM responses.

Build intelligent AI agents using Microsoft Semantic Kernel with tool calling, memory, and multi-agent coordination in .NET.

Implementing model policy management and global token accounting so agentic features remain cost-aware and governable.

Implement a complete Retrieval-Augmented Generation pipeline that combines semantic search with local LLM inference for intelligent document Q&A.

Build a powerful hybrid search system that combines vector embeddings with traditional keyword search for comprehensive document retrieval.

Bridging the gap between design and development by using the Model Context Protocol (MCP) to automate UI generation from Figma to Blazor.

Create a production-ready embedding pipeline in .NET that generates vector embeddings with Ollama and stores them in Qdrant for semantic search.

Learn how to integrate Docling, an AI-powered document understanding library, into your .NET application for high-quality OCR with layout preservation.