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.
A guide to fine-tuning embedding models for RAG applications using Sentence Transformers 3, featuring Matryoshka Representation Learning for efficiency.
An experiment comparing retrieval performance of chunked vs. non-chunked documents using long-context embedding models like BGE-M3.
A practical guide sharing lessons learned from a year of building real-world applications with Large Language Models (LLMs).
A technical guide on building a Retrieval-Augmented Generation (RAG) system in Go to query PostgreSQL tabular data using Google's Gemini LLM.
A technical guide on using Azure AI Language Studio to summarize and optimize grounding documents for improving RAG-based AI solutions.
Explains how to implement document retrieval with the Azure OpenAI Assistants API using a custom RAG approach, as the retrieval tool is not yet natively supported.
A simple explanation of Retrieval-Augmented Generation (RAG), covering its core components: LLMs, context, and vector databases.
