# RAG Published articles for RAG. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## Building a RAG Pipeline for Semantic Code Search: A Developer Diary and Field Notes DevFeed: [Building a RAG Pipeline for Semantic Code Search: A Developer Diary and Field Notes]() Original publisher: [Read original article]() Author: Adam Malek Published: 2026-09-17T12:39:40Z Content type: article Language: en Sources: [The JetBrains Blog]() Topics: [Retrieval Augmented Generation (RAG)](), [code search](), [Parsing](), [jetbrains](), [Large Language Model]() Tags: [agentic-ai](), [ai](), [code-search](), [jetbrains](), [llm](), [llm-agents](), [parsing](), [rag](), [search](), [semantic]() ### AI overview Part 1 of a developer diary explains how JetBrains built a RAG pipeline for semantic code search, covering parsing, chunking, and vectorization. The pipeline is intended to give LLM agents precise, citable evidence from real repositories and retrieve code by meaning rather than exact keywords. ### Source excerpt Part 1: Parsing, chunking, and vectorization Some time ago, we set out to build the best semantic code search platform we could: a RAG pipeline that gives LLM agents precise, citable evidence from real repositories instead of whatever grep happens to surface. The eventual solution was JetBrains Context. We got it working, we got it [...] ## MLPerf Inference v6.1: 5.7x Per-Accelerator Gains, a 512-GPU Run, and Vera Rubin's First Peer-Reviewed Numbers DevFeed: [MLPerf Inference v6.1: 5.7x Per-Accelerator Gains, a 512-GPU Run, and Vera Rubin's First Peer-Reviewed Numbers]() Original publisher: [Read original article]() Author: Harold Fritts Published: 2026-09-16T15:00:00Z Content type: news Language: en Sources: [StorageReview.com]() Topics: [Benchmark](), [Inference Performance](), [Retrieval Augmented Generation (RAG)](), [agentic-coding](), [GPU](), [Vera Rubin NVL72](), [Nvidia](), [Vera Rubin]() Tags: [agentic-coding](), [ai](), [benchmark](), [enterprise](), [gpt-oss](), [gpu](), [inference](), [llama-cpp](), [numbers](), [nvidia](), [qwen3](), [rag]() ### AI overview MLCommons published MLPerf Inference v6.1 with record participation, two new inference tests, and peer-reviewed results for several newly covered accelerators. The release reports a 5.7x improvement in the best per-accelerator DeepSeek-R1 server result compared with v5.1. ### Source excerpt MLCommons has published MLPerf Inference v6.1, and the round sets a participation record with 30 submitting organizations and 486 datacenter and edge results. Two new tests join the suite: an End-to-End RAG pipeline for the datacenter and an Edge Agentic Inference benchmark for single-user devices, and the results carry the first peer-reviewed numbers for NVIDIA's The post MLPerf Inference v6.1: 5.7x Per-Accelerator Gains, a 512-GPU Run, and Vera Rubin's First Peer-Reviewed Numbers appeared first on StorageReview.com. ## Dropbox Evolves Riviera Content Processing Platform to Support AI Workloads DevFeed: [Dropbox Evolves Riviera Content Processing Platform to Support AI Workloads]() Original publisher: [Read original article]() Author: Leela Kumili Published: 2026-09-16T14:42:00Z Content type: news Language: en Sources: [InfoQ]() Topics: [dropbox](), [Artificial Intelligence](), [API](), [Orchestration](), [Retrieval Augmented Generation (RAG)]() Tags: [ai](), [ai-ml-data-engineering](), [apache](), [apis](), [architecture](), [architecture-design](), [asynchronous-architecture](), [backend](), [caching](), [data-pipelines](), [development](), [distributed-systems](), [dropbox](), [dropbox-riviera-ai-platform](), [embeddings](), [enterprise-content-management](), [large-language-models](), [ml-data-engineering](), [model-context-protocol-mcp](), [news](), [orchestration](), [platform-engineering](), [plugins](), [rag](), [tika]() ### AI overview Dropbox has expanded Riviera from an internal file-preview service into a content-processing platform supporting more than 300 file formats and over 100 transformation capabilities. The platform supports Dropbox products including Search, Replay, Sign, and Dash, and provides APIs for asynchronous document conversion, media transcription, and structured metadata extraction for AI and RAG workflows. ### Source excerpt Dropbox has evolved Riviera from a file preview service into a universal content processing platform supporting more than 300 file formats and over 100 transformation capabilities. Processing hundreds of thousands of transformations per second, Riviera now supports Search, Replay, Sign, and Dash, while its APIs enable asynchronous content extraction for AI and RAG workflows. By Leela Kumili ## How Everpure proposes reducing GPU idle time by improving AI data access DevFeed: [How Everpure proposes reducing GPU idle time by improving AI data access]() Original publisher: [Read original article]() Author: Chris Mellor Published: 2026-09-15T08:00:00Z Content type: article Language: en Sources: [www.theregister.com - Articles]() Topics: [AI Infrastructure](), [GPU](), [Large Language Model](), [Retrieval Augmented Generation (RAG)](), [AI Agent](), [data-processing](), [data]() Tags: [agents](), [ai](), [ai-infrastructure](), [ai-ml](), [data-processing](), [gpu](), [hardware](), [rag](), [sponsored]() ### AI overview This sponsored feature describes Everpure's approach to reducing GPU idle time in AI systems by improving access to large-scale insurance data. It discusses central metadata indexing, storage performance, self-describing data, and integration with Nvidia GPU infrastructure for AI agents and retrieval-augmented generation. ### Source excerpt SPONSORED FEATURE: The vendor's AI solutions are dedicated to increasing GPU utilization and avoiding costly GPUs doing nothing while waiting for data ## Unified Knowledge Graph RAG on AWS: GraphRAG and LightRAG on one stack DevFeed: [Unified Knowledge Graph RAG on AWS: GraphRAG and LightRAG on one stack]() Original publisher: [Read original article]() Author: Jonas Kim Published: 2026-09-14T16:55:58Z Content type: article Language: en Sources: [AWS Open Source Blog]() Topics: [Retrieval Augmented Generation (RAG)](), [Graphs](), [Amazon Bedrock](), [Amazon OpenSearch Service](), [Amazon Web Services]() Tags: [amazon-bedrock](), [amazon-opensearch-service](), [aws](), [knowledge-graph](), [open-source](), [rag](), [retrieval](), [search](), [vector-search]() ### AI overview This article presents a unified knowledge-graph RAG stack that brings Microsoft GraphRAG and HKUDS LightRAG together on Amazon Bedrock, Amazon Neptune, and Amazon OpenSearch Service. The shared stack supports common ingestion, indexing, caching, and multilingual handling while allowing the retrieval methodology to be selected per query. ### Source excerpt Picture a compliance analyst staring at a few thousand contracts, amendments, and internal memos, trying to answer one question that sounds straightforward: "Which of our obligations are exposed if this one milestone slips?" The answer isn't written in any single document. It's stitched across three -- a master agreement that ties a payment to a [...] ## The generative AI customization spectrum: From prompt engineering to custom models on AWS DevFeed: [The generative AI customization spectrum: From prompt engineering to custom models on AWS]() Original publisher: [Read original article]() Author: Bhavya Sruthi Sode Published: 2026-09-14T15:47:12Z Content type: tutorial Language: en Sources: [Artificial Intelligence]() Topics: [Generative AI](), [Amazon Web Services](), [Prompt Engineering](), [Fine-tuning](), [Retrieval Augmented Generation (RAG)](), [Amazon Bedrock](), [foundation-models](), [Anthropic Claude](), [Nova](), [llama]() Tags: [advanced-300](), [ai](), [amazon-bedrock](), [anthropic-claude](), [aws](), [best-practices](), [chain-of-thought](), [fine-tuning](), [foundation-models](), [generative-ai](), [llama](), [nova](), [optimization](), [prompt-engineering](), [rag]() ### AI overview This AWS article presents an eight-step decision framework for customizing generative AI workloads. It compares progressively more involved approaches, including prompt engineering, Retrieval Augmented Generation (RAG), fine-tuning, continued pre-training, and custom models such as Amazon Nova Forge, emphasizing that teams should start with the simplest approach and escalate when greater control or domain specificity is required. ### Source excerpt Pick the right generative AI customization approach on AWS with an 8-step decision framework, from prompt engineering and RAG to fine-tuning, continued pre-training, and Amazon Nova Forge. Start simple and escalate only when you must. ## Не трогая веса модели: как мы построили исследовательского агента Алисы AI и в разы сократили потребление GPU DevFeed: [Не трогая веса модели: как мы построили исследовательского агента Алисы AI и в разы сократили потребление GPU]() Original publisher: [Read original article]() Author: prohor33 (Яндекс) Published: 2026-09-14T08:01:22Z Content type: article Language: ru Sources: [Яндекс - Как мы делаем Яндекс / Статьи]() Topics: [Artificial Intelligence](), [Retrieval Augmented Generation (RAG)](), [SDKs](), [Large Language Model]() Tags: [agent](), [ai](), [ai-867179ebf949](), [deepresearch](), [llm](), [rag](), [research](), [sdk](), [tag-14fcd5db6179](), [tag-1605473766c5](), [tag-6237da65686b]() ### AI overview The article describes how the Alice AI team built and deployed a Deep Research agent without changing the model weights. It traces the evolution from a reasoning mode with a multi-query RAG pipeline into a full agent capable of planning research, searching the web, handling dynamic JavaScript content, running Python code, and working with downloaded files. It also discusses production rollout, answer quality, latency, and GPU consumption. ### Source excerpt Меня зовут Прохор, я лид команды агента "Исследовать" -- это режим глубокого исследования в чате с Алисой AI. Напомню, про что вообще речь, если никогда не пользовались Deep Research: это специальный режим работы, который строит уникальный план решения задачи пользователя, делает сотни поисков по вашему запросу, умеет ходить на сайты (даже с динамическим JavaScript-контентом), писать и выполнять Python-код (для сложных расчётов), работать со скачанными файлами и так далее. Всё это для того, чтобы дать лучший ответ на ваши сложные запросы, например: "Спланируй мне путешествие в Дагестан на две недели на машине с детьми". За год агент прошёл путь от первого прототипа до продакшена -- вместе с ним менялись качество ответов, скорость работы и потребление GPU. За продуктовую часть отвечал Руслан Илиев, продакт менеджер агента: он сформулировал продуктовые цели, определил набор инструментов и валидационный набор запросов, а затем вёл запуск от закрытого вейтлиста до 100% продакшена. Как мы к этому пришли -- через выброшенный прототип, десятки слоёв обвязки и пару болезненных уроков, -- расскажу по порядку. Добро пожаловать под кат! Читать далее ## Presentation: From Retrieval to Reasoning: Building Production-Ready Agentic AI Systems with Knowledge Graphs DevFeed: [Presentation: From Retrieval to Reasoning: Building Production-Ready Agentic AI Systems with Knowledge Graphs]() Original publisher: [Read original article]() Author: Cassie Shum Published: 2026-09-12T11:00:00Z Content type: tutorial Language: en Sources: [InfoQ]() Topics: [Graphs](), [Architecture & Design](), [Retrieval Augmented Generation (RAG)](), [Code]() Tags: [agentic-ai](), [agentic-ai-architecture](), [agents](), [ai](), [ai-architecture](), [ai-ml-data-engineering](), [architecture](), [architecture-design](), [artificial-intelligence](), [code](), [development](), [engineering](), [infoq](), [knowledge-graph](), [knowledge-graphs-agentic-systems-patterns](), [large-language-models](), [ml-data-engineering](), [presentation](), [production](), [qcon-ai-boston-2026](), [qcon-software-development-conference](), [rag](), [reasoning](), [retrieval-augmented-generation](), [transcripts]() ### AI overview A presentation on using knowledge graphs as a foundation for production-ready agentic AI systems. It covers architectural patterns for context bundling, decision provenance, code as truth, and agent visibility, along with a graph-based engineering harness for feedback loops, token optimization, and reliability. ### Source excerpt Cassie Shum discusses why knowledge graphs serve as a critical foundation for agentic systems. Moving beyond basic RAG, she explains 4 practical architectural patterns: context bundling, decision provenance, code as truth, and agent visibility. She demonstrates an engineering harness built on a knowledge graph to streamline feedback loops, optimize token usage, and maintain system reliability. By Cassie Shum ## Bringing custom knowledge to agents with AutoRAG DevFeed: [Bringing custom knowledge to agents with AutoRAG]() Original publisher: [Read original article]() Author: Diego Alvarez Ponce, Diego Garcia Perez Published: 2026-09-11T13:01:50Z Content type: article Language: en Sources: [Red Hat](), [Red Hat Developer]() Topics: [Retrieval Augmented Generation (RAG)](), [Model Context Protocol (MCP)](), [Databases](), [Large Language Model](), [AI Development](), [LLM evaluation / benchmarking]() Tags: [ai](), [ai-inference](), [artificial-intelligence](), [database](), [developer](), [generative-ai](), [gpu](), [guide](), [how-to](), [large-language-models-llms](), [model-context-protocol-mcp](), [rag](), [red-hat](), [terminal]() ### AI overview This guide explains how to use AutoRAG in Red Hat OpenShift AI to optimize retrieval-augmented generation for enterprise, domain-specific data. It also shows how to integrate a Model Context Protocol server that retrieves customer information from an internal database, using a fictional banking scenario. ### Source excerpt The large language models (LLMs) that power generative AI work by drawing upon the patterns and information present in their training data. Without access to the right data, LLMs struggle to comprehend context--like our internal corporate vocabulary--and as a result, start to hallucinate. The post Bringing custom knowledge to agents with AutoRAG appeared first on Red Hat Developer. ## AI Functions in ClickHouse: Upgrade your SQL to the AI age DevFeed: [AI Functions in ClickHouse: Upgrade your SQL to the AI age]() Original publisher: [Read original article]() Author: Andriy Yakovlev; George Larionov Published: 2026-09-11T12:49:32Z Content type: article Language: en Sources: [ClickHouse Blog]() Topics: [clickhouse](), [Large Language Model](), [AI search](), [Retrieval Augmented Generation (RAG)](), [Databases]() Tags: [ai](), [classification](), [clickhouse](), [embeddings](), [llm](), [rag](), [sql]() ### AI overview ClickHouse introduces beta AI Functions that invoke LLM and embedding providers directly from SQL for tasks including classification, extraction, generation, translation, filtering, redaction, embeddings, and semantic similarity. ### Source excerpt Explore ClickHouse AI Functions for classification, generation, translation, embeddings, semantic search, and cost controls--all directly from SQL. ## Put Redis data and engineering guidance to work in ChatGPT Work DevFeed: [Put Redis data and engineering guidance to work in ChatGPT Work]() Original publisher: [Read original article]() Author: Olga Lopaci Published: 2026-09-11T00:00:00Z Content type: release Language: en Sources: [Redis Blog]() Topics: [Developer Tools](), [AI Chat](), [AI Bots]() Tags: [agent-skills](), [chatgpt](), [codex](), [engineering](), [observability](), [rag](), [redis](), [review](), [security](), [skills](), [tech](), [work]() ### AI overview Redis launched a development plugin for ChatGPT Work and Codex that supplies current Redis guidance for writing, reviewing, and troubleshooting code. It also describes connecting Redis data to ChatGPT Work's Data agent for plain-language exploration and investigation. ### Source excerpt Redis has launched a development plugin that brings current Redis engineering guidance into ChatGPT Work and Codex. It helps teams write, review, and troubleshoot Redis code without switching between documentation and development tools. Alongside Ope... ## Video and image search in Amazon Bedrock Knowledge Base using Marengo 3.0 DevFeed: [Video and image search in Amazon Bedrock Knowledge Base using Marengo 3.0]() Original publisher: [Read original article]() Author: Eric Kim Published: 2026-09-10T21:15:39Z Content type: tutorial Language: en Sources: [Artificial Intelligence]() Topics: [AI search](), [Retrieval Augmented Generation (RAG)](), [Amazon S3](), [AWS IAM]() Tags: [amazon-bedrock](), [amazon-bedrock-knowledge-bases](), [announcements](), [audio](), [embedding](), [how-to](), [images](), [intermediate-200](), [multimodal](), [rag](), [s3](), [search](), [video]() ### AI overview A walkthrough for building an Amazon Bedrock Knowledge Base with TwelveLabs Marengo Embed 3.0 to perform natural-language semantic search across video, images, and audio. ### Source excerpt TwelveLabs Marengo Embed 3.0 is now generally available as an embedding model in Amazon Bedrock Knowledge Bases, bringing fully managed natural language search to video, image, and audio content. This walkthrough shows how to build a knowledge base powered by Marengo 3.0 and run semantic queries against your media. ## Build a support chatbot with Appwrite Functions and VectorsDB DevFeed: [Build a support chatbot with Appwrite Functions and VectorsDB]() Original publisher: [Read original article]() Author: Atharva Deosthale Published: 2026-09-10T00:00:00Z Content type: tutorial Language: en Sources: [Appwrite Blog]() Topics: [Appwrite](), [Chat Bot](), [Retrieval-Augmented Generation](), [Embeddings](), [React](), [SDKs](), [Node.js]() Tags: [api](), [build](), [chat](), [deploy](), [embedding](), [functions](), [node-js](), [rag](), [react](), [retrieval](), [support](), [tutorials]() ### AI overview This tutorial builds Harbor Help, a support chatbot for a fictional project-management product. It uses Appwrite VectorsDB to store help-center article embeddings, an Appwrite Function to retrieve relevant excerpts and generate answers through OpenRouter, and Appwrite Sites to host a React interface. The chatbot uses retrieval-augmented generation and can refuse questions outside the help center. ### Source excerpt Seed a help center, retrieve relevant articles with VectorsDB, and answer customer questions through an Appwrite Function. Deploy the chat interface on Appwrite Sites. ## Rust AI in Practice: Building LLM Applications With Rig DevFeed: [Rust AI in Practice: Building LLM Applications With Rig]() Original publisher: [Read original article]() Author: Irina Mihajlovic Published: 2026-09-09T12:02:05Z Content type: article Language: en Sources: [The JetBrains Blog]() Topics: [Language models](), [Local AI](), [AI-assisted coding]() Tags: [ai](), [ai-agents](), [anthropic](), [applications](), [gemini](), [livestream](), [llm](), [llm-aplications](), [openai](), [rag](), [rust](), [rust-ai](), [rustrover]() ### AI overview The article introduces Rig, a Rust library that provides a unified interface for building LLM applications across providers. It uses a livestream coding-agent demo to explain Rig's provider clients, models, agents, tools, and prompts. ### Source excerpt Rust AI is moving from the experimentation stage toward more practical usage. We recently kicked off a new livestream series with the Rust Foundation to explore how Rust and AI are coming together in real-world applications. In the first session, our Developer Advocate Orhun Parmaksız spoke with Stephen Korzeniewski, Lead Maintainer of Rig at 0xPlaygrounds. [...] ## Full-Text Search, Object Storage Backend, and More in ScyllaDB 2026.3 DevFeed: [Full-Text Search, Object Storage Backend, and More in ScyllaDB 2026.3]() Original publisher: [Read original article]() Author: Tzach Livyatan Published: 2026-09-08T21:32:47Z Content type: release Language: en Sources: [ScyllaDB]() Topics: [releases](), [Amazon S3](), [AI search](), [Retrieval Augmented Generation (RAG)]() Tags: [ai](), [product](), [rag](), [release](), [s3](), [search](), [storage]() ### AI overview ScyllaDB 2026.3 adds full-text search, a preview object-storage backend, OCI integration, and experimental table and migration capabilities. It also introduces large-data guardrails and cluster-wide restoration from object-storage backups. ### Source excerpt new updates should help you move even more workloads to ScyllaDB, at a fraction of the cost. ## Progressive Authentication for Production-Ready RAG Architectures DevFeed: [Progressive Authentication for Production-Ready RAG Architectures]() Original publisher: [Read original article]() Author: Aishwarya Murali Padikkal Published: 2026-09-08T07:16:00Z Content type: article Language: en Sources: [Data Science at Microsoft]() Topics: [Authentication](), [Retrieval Augmented Generation (RAG)](), [sensitive data](), [User Experience](), [Security]() Tags: [ai](), [ai-agent](), [authentication](), [data-science](), [privacy](), [rag](), [retrieval](), [sensitive-data](), [user-experience]() ### AI overview The article presents progressive authentication for RAG systems: answer public questions without sign-in, then authenticate within the conversation when a request requires access to sensitive personal data. It argues that static, upfront authentication does not fit conversations that shift between public and private contexts. ### Source excerpt Designing a production-ready RAG architecture where authentication becomes part of the conversation, not a prerequisite for it. By Aishwarya Murali Padikkal & Priyank Solanki From understanding to retrieval, grounding, and response, authentication enters only when the conversation requires access to sensitive data- illustrating the core idea of progressive authentication. Illustration generated using ChatGPT and refined by the authors. Two questions, one help line Imagine two taxpayers opening the same AI-powered tax assistant. The first asks, "When is the filing deadline this year?" The second asks, "What was my refund last year?" Both questions are valid. Both deserve fast, helpful answers. Yet they represent fundamentally different types of requests. Figure 1. The same assistant, two different journeys. Requests are routed according to the type of information being accessed. Illustration generated by AI (ChatGPT) and refined by the authors. The answer to the first lives in a public handbook. Anyone should be able to access it instantly- no sign-in, no friction. The answer to the second is within an individual's private tax records. Before revealing a single detail, the assistant must be certain it is interacting with the right person. Making public information instantly accessible while ensuring private information is revealed only to the right person, all within the same conversation, is the challenge we set out to solve. It is also where conventional RAG systems quietly fall apart. Traditional RAG architectures assume a much simpler interaction model: a user asks a question, the system retrieves relevant documents, and the model generates an answer. Authentication, if it exists at all, is treated as a one-time checkpoint at the start of the interaction. Real conversations are far less predictable. Users naturally shift contexts, starting with a public question, moving to a personal one, and then asking follow-up questions that rely on both. The conversation evo ## Build an AI Agent (From Scratch) DevFeed: [Build an AI Agent (From Scratch)]() Original publisher: [Read original article]() Author: Nicolas Fränkel Published: 2026-09-06T00:00:00Z Content type: opinion Language: en Sources: [Nicolas Fränkel]() Topics: [AI Agent](), [Large Language Model](), [Model Context Protocol (MCP)](), [Retrieval Augmented Generation (RAG)](), [Multi Agent Systems]() Tags: [ai](), [ai-agent](), [ai-agents](), [book](), [claude-code](), [coding-assistant](), [genealogy](), [llm](), [llms](), [mcp](), [multi-agent-systems](), [rag](), [review]() ### AI overview A review of Build an AI Agent (From Scratch) by Jungjun Hur and Younghee Song describes the book's coverage of LLM agents, tool use, ReAct agents, RAG, memory, planning, code execution, multi-agent systems, and agent evaluation. The reviewer considers it a fundamental introduction, while wishing the evaluation chapter were more developed. ### Source excerpt Facts 10 chapters$29.29 (at the time of this writing)315 pages Chapters Building your first LLM agentWhat is an AI agent? ## EP224: MCP vs RAG vs AI Agents DevFeed: [EP224: MCP vs RAG vs AI Agents]() Original publisher: [Read original article]() Author: ByteByteGo Published: 2026-09-05T15:30:58Z Content type: article Language: en Sources: [ByteByteGo]() Topics: [AI Agent](), [Model Context Protocol](), [Retrieval Augmented Generation (RAG)](), [distributed-systems](), [Publish-subscribe pattern](), [Replication]() Tags: [ai-agent](), [distributed-systems](), [mcp](), [messaging](), [rag](), [replication]() ### AI overview This article compares MCP, RAG, and AI agents. It explains MCP as a standard protocol for connecting AI models to external tools and data, RAG as a way to retrieve current information from external sources, and AI agents as systems that perform tasks and make decisions autonomously. It also summarizes several distributed-systems patterns, including replication, sharding, publish-subscribe, circuit breakers, retries, and leader election. ### Source excerpt An AI agent is kind of an AI system where the agent performs the task autonomously and takes the decisions. ## How Embedding Models Affect Retrieval-Augmented Generation Quality DevFeed: [How Embedding Models Affect Retrieval-Augmented Generation Quality]() Original publisher: [Read original article]() Author: ByteByteGo Published: 2026-09-02T15:31:12Z Content type: tutorial Language: en Sources: [ByteByteGo]() Topics: [Embeddings](), [Retrieval Augmented Generation (RAG)](), [Language models](), [Artificial Intelligence]() Tags: [embedding](), [embeddings](), [model](), [rag](), [retrieval-augmented-generation]() ### AI overview This article explains how embedding models support retrieval-augmented generation by translating words into vectors and controlling the search for relevant information before an answer is generated. It discusses why poor retrieval can produce incorrect answers even when the language model is capable, and outlines considerations for evaluating and selecting embedding models. ### Source excerpt In this article, we're going to look at how this embedding model works in an RAG setup and what makes it such a critical part of the system. ## Deploying a RAG Chatbot with Shared State and Storage Across Replicas DevFeed: [Deploying a RAG Chatbot with Shared State and Storage Across Replicas]() Original publisher: [Read original article]() Author: Avi Chawla Published: 2026-09-01T21:11:23Z Content type: tutorial Language: en Sources: [Daily Dose of Data Science]() Topics: [Retrieval-Augmented Generation](), [Kubernetes](), [Terraform](), [FastAPI](), [LangChain](), [Python]() Tags: [fastapi](), [github](), [persistence](), [pgvector](), [python](), [rag](), [retrieval](), [terraform]() ### AI overview The article explains why a RAG chatbot that works locally can lose vector indexes, conversation history, and documents when deployed across multiple replicas. It recommends shared persistence for embeddings, checkpointed conversation state, and shared object storage, with examples using Postgres, pgvector, LangGraph, and object storage. ### Source excerpt + a popular LLM interview question. ## Why Your AI Application Is Exposed DevFeed: [Why Your AI Application Is Exposed]() Original publisher: [Read original article]() Author: Damian Tommasino Published: 2026-08-26T04:00:00Z Content type: article Language: en Sources: [Blog RSS Feed | Snyk]() Topics: [Application Security](), [AI Development](), [Large Language Model](), [vulnerability](), [Model Context Protocol](), [Retrieval Augmented Generation (RAG)](), [Machine Learning, Security Attacks](), [data]() Tags: [ai](), [ai-red-team](), [application-security](), [awareness](), [blog](), [devops](), [executive](), [interest](), [llm](), [mcp](), [rag](), [security](), [testing](), [vulnerability]() ### AI overview The article explains why AI applications can pass conventional security scans and model evaluations while remaining exploitable through chained interactions among prompts, language models, retrieval, tools, and backend workflows. It presents DAST, AI penetration testing, and AI red teaming as complementary lenses for identifying exposed surfaces, exploitable paths, and real-world adversary outcomes. ### Source excerpt AI applications can pass security scans yet remain exploitable through chained attacks across models, tools, data, and business workflows. Learn how DAST, AI pentesting, and red teaming work together to expose end-to-end risk. ## Start here: Azure SQL Foundations series DevFeed: [Start here: Azure SQL Foundations series]() Original publisher: [Read original article]() Author: Anna Hoffman Published: 2026-08-25T16:00:18Z Content type: article Language: en Sources: [Developer Blogs]() Topics: [Azure SQL](), [Microsoft](), [migration](), [Artificial Intelligence](), [Azure OpenAI](), [DevOps](), [GitHub](), [Retrieval Augmented Generation (RAG)](), [API](), [MCP Server]() Tags: [ai](), [azure](), [azure-openai](), [azure-sql](), [devops](), [github](), [guide](), [how-to](), [json](), [langchain](), [mcp](), [microsoft-for-developers](), [migration](), [powershell](), [rag](), [terraform]() ### AI overview Microsoft introduces the Azure SQL Database Foundations series, a set of four videos covering Hyperscale databases, modernization and migration, AI features, and operational data. Each episode includes a repository for hands-on follow-up. ### Source excerpt Most developers I talk to aren't asking whether Azure SQL Database can handle their next app. They're asking where to start when it comes to modernization, migration, and AI in the database. If you're reading this, you're probably in a similar boat: you've got an existing schema or databases, a scaling question you haven't had [...] The post Start here: Azure SQL Foundations series appeared first on Microsoft for Developers. ## Meet editorial-guide-ramalama: An AI Assistant That Checks Your Fedora CommBlog and Magazine Articles Against Editorial Guidelines DevFeed: [Meet editorial-guide-ramalama: An AI Assistant That Checks Your Fedora CommBlog and Magazine Articles Against Editorial Guidelines]() Original publisher: [Read original article]() Author: Ananya Nalavathu Published: 2026-08-25T12:47:18Z Content type: article Language: en Sources: [Fedora Community Blog]() Topics: [Fedora](), [Retrieval Augmented Generation (RAG)](), [Artificial Intelligence](), [Local AI](), [Containers](), [Open Source](), [Tooling](), [On-device AI]() Tags: [ai](), [articles](), [blog](), [fedora-project-community](), [inference](), [local](), [mentored-projects](), [oci](), [open-source](), [rag](), [tool]() ### AI overview The article introduces editorial-guide-ramalama, a Retrieval-Augmented Generation assistant built for Fedora contributors. It checks drafts against Fedora's editorial guidelines and published articles, cites specific guidelines when it identifies problems, and suggests actionable fixes. The tool uses RamaLama to run open models locally as OCI containers, with built-in ingestion, chunking, and retrieval, avoiding API keys and external services. ### Source excerpt By Ananya Nalavathu and Francois Gonothi Toure Introduction Open source communities run on contribution, and contribution runs on documentation, storytelling, and knowledge sharing. At the Fedora Project, that means the Fedora Community Blog and Fedora Magazine, two publications that give contributors a voice and give the community a way to stay informed, inspired, and connected. [...] The post Meet editorial-guide-ramalama: An AI Assistant That Checks Your Fedora CommBlog and Magazine Articles Against Editorial Guidelines appeared first on Fedora Community Blog. ## Preloading Knowledge Into a Model Instead of Retrieving It DevFeed: [Preloading Knowledge Into a Model Instead of Retrieving It]() Original publisher: [Read original article]() Author: Avi Chawla Published: 2026-08-24T17:13:39Z Content type: tutorial Language: en Sources: [Daily Dose of Data Science]() Topics: [Retrieval Augmented Generation (RAG)](), [Caching](), [long-context](), [Inference](), [Self-hosted](), [model-deployment](), [GPU]() Tags: [caching](), [compute](), [context-window](), [deployment](), [inference](), [rag](), [retrieval](), [self-hosted](), [storage]() ### AI overview This article introduces a three-part RAG Systems course about preloading a knowledge corpus into a model's stored KV cache so subsequent queries can skip retrieval, chunking, and embedding. It covers naive, compressed, modular, and trained preloading, along with context limits, provider and self-hosted economics, cache compression constraints, and production deployment. ### Source excerpt How to process your corpus once, skip retrieval entirely, and serve every query from a stored cache. Three parts covering the full spectrum. [Next page]()