A developer's experience building a RAG app using Google Antigravity AI coding assistant, Gemini 3 Pro, Angular, and Spring AI.
A guide to building a connector-based RAG system that fetches live data from Confluence using its REST API and Java, avoiding stale embeddings.
A guide to building a local, privacy-focused RAG system using Java to query internal documents like Confluence without external dependencies.
A guide for .NET developers to build an AI chat app with RAG and image generation using .NET, MCP, and Hugging Face in under 10 minutes.
A guide on preparing data for Generative AI using RAG, covering data embedding, chunking, and building effective data pipelines.
Explores three methods to automate security questionnaire responses using LLMs, from SaaS vendors to custom RAG systems and direct ChatGPT/Claude use.
Guide on integrating Microsoft 365 Copilot agents with Azure AI Search for enhanced knowledge retrieval using RAG, offering more control than basic options.
A tutorial on building a Retrieval-Augmented Generation (RAG) server using IBM Watsonx.ai, ChromaDB, and the Model Context Protocol (MCP) Python SDK.
A tutorial on integrating IBM watsonx.ai models into Langflow to build visual RAG applications and AI workflows.
Explores AI agents, their core components, differences from LLMs, and real-world applications, positioning them as the future of autonomous AI systems.
A summary of Chip Huyen's chapter on AI fine-tuning, arguing it's a last resort after prompt engineering and RAG, detailing its technical and organizational complexities.
Analysis of Chapter 6 from Chip Huyen's 'AI Engineering' book, focusing on RAG systems and AI agents, their architecture, costs, and relationship.
Argues that RAG system failures stem from data engineering issues like fragmented data and governance, not from model or vector database choices.
Argues that building a good search engine is more critical for effective RAG than just using a vector database, as poor retrieval misleads AI.
Explores using Azure Logic Apps for document parsing and chunking to streamline RAG (Retrieval-Augmented Generation) workflows and AI integration.
A tutorial on implementing a local RAG system using Phi-3, Semantic Kernel, and TextMemory in a C# console application.
An experiment comparing retrieval performance of chunked vs. non-chunked documents using long-context embedding models like BGE-M3.
