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.
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.
Explores how AI can revolutionize communication by bridging context gaps between people, using tools like RAG and AI assistants as proxies.
A guide to fine-tuning and deploying custom embedding models for RAG applications on Amazon SageMaker using Sentence Transformers v3.
A tutorial on implementing a local RAG system using Phi-3, Semantic Kernel, and TextMemory in a C# console application.
Explains the limitations of Large Language Models (LLMs) and introduces Retrieval Augmented Generation (RAG) as a solution for incorporating proprietary data.
