A guide to choosing LLMs for enterprise RAG, emphasizing retrieval fit over model hype, with practical selection criteria.
A guide to building enterprise RAG systems that prioritize evidence over confident answers, covering retrieval, reranking, and citation validation.
Case study on building production-ready agentic AI systems for pharmaceutical drug development using RAG and Text-to-SQL.
Explains grounding in LLMs: connecting them to reliable data for accurate, context-aware responses using techniques like RAG and fine-tuning.
Announcing a talk at Adriatics Tech Summit 2026 on implementing RAG with Microsoft.Extensions.VectorData in .NET.
Explores building a multi-agent AI system for personalized recommendations and task execution using OpenClaw and local agents.
A tutorial on using the Gemini API's File Search feature for RAG in web development with JavaScript/TypeScript.
Explains Retrieval-Augmented Generation (RAG), a pattern for improving LLM accuracy by augmenting prompts with retrieved context.
Introduces Graphiti, an open-source framework for building bi-temporal knowledge graphs to give AI agents long-term memory and real-time data understanding.
A hands-on guide for JavaScript developers to learn Generative AI and LLMs through interactive lessons, projects, and a companion app.
Explores AI agents, their core components, differences from LLMs, and real-world applications, positioning them as the future of autonomous AI systems.
Explores three key methods to enhance LLM performance: fine-tuning, prompt engineering, and RAG, detailing their use cases and trade-offs.
Explains a technique using AI-generated summaries of SQL queries to improve the accuracy of text-to-SQL systems with LLMs.
Explores AI engineering architecture patterns and user feedback methods, from simple APIs to complex agent-based systems.
Analysis of Chapter 6 from Chip Huyen's 'AI Engineering' book, focusing on RAG systems and AI agents, their architecture, costs, and relationship.
An overview of Azure AI Foundry, a unified platform for building and deploying AI solutions on Microsoft Azure, covering its features and benefits.
Explores Microsoft's Graph RAG, an advanced RAG technique using knowledge graphs to answer global questions about datasets, with a hands-on setup guide.
Explains the limitations of Large Language Models (LLMs) and introduces Retrieval Augmented Generation (RAG) as a solution for incorporating proprietary data.
Explains how to use Azure OpenAI with your own data via Semantic Kernel, focusing on RAG and Azure AI Search integration.
A technical guide on using Azure AI Language Studio to summarize and optimize grounding documents for improving RAG-based AI solutions.
