A developer shares his experience using an Elgato Prompter for video content and creating a custom 3D-printed iPhone 17 Pro backplate for it.
A technical guide on deploying the KAITO RAG Engine for AI-powered retrieval-augmented generation on Azure Kubernetes Service (AKS).
Author begins a blog series on learning Azure AI for certification, covering services like OpenAI, RAG, and Generative AI with practical .NET examples.
Explains how to use the KAITO RAG Engine on Azure Kubernetes Service to build a Retrieval-Augmented Generation (RAG) system for querying private documents with LLMs.
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.
Explains why Context Engineering, not just prompt crafting, is the key skill for building effective AI agents and systems.
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 integrating IBM watsonx.ai models into Langflow to build visual RAG applications and AI workflows.
A tutorial on building a Retrieval-Augmented Generation (RAG) server using IBM Watsonx.ai, ChromaDB, and the Model Context Protocol (MCP) Python SDK.
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.
