Explains why policy-as-code, not RAG, is the key to secure enterprise AI by embedding authorization into query engines.
A guide comparing vector stores like pgvector, Milvus, Weaviate, and LanceDB for retrieval workloads, focusing on index types and tradeoffs.
Explains how to design governed RAG systems using data products, separating retrieval and governance for accurate, policy-compliant AI responses.
Explores building AI agents using RAG, tool calling, and memory, with practical insights from a key paper and Anthropic's SDK.
Explains grounding in LLMs: connecting them to reliable data for accurate, context-aware responses using techniques like RAG and fine-tuning.
A critique of an AI shopping assistant's failure to answer a product question, highlighting the superiority of simple keyword search over retrieval-based AI.
Announcing a talk at Adriatics Tech Summit 2026 on implementing RAG with Microsoft.Extensions.VectorData in .NET.
A guide comparing strategies for providing context to AI models in workflows, analyzing tradeoffs in cost, latency, and observability.
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.
Explains Retrieval-Augmented Generation (RAG), a pattern for improving LLM accuracy by augmenting prompts with retrieved context.
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.
