# Retrieval Augmented Generation (RAG) A language-generation technique that retrieves relevant information from an external non-parametric memory and uses it as context for a pretrained model. 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 ## Article: Your Next DSL Author Is a Language Model DevFeed: [Article: Your Next DSL Author Is a Language Model]() Original publisher: [Read original article]() Author: Irakli Betchvaia Published: 2026-09-16T11:00:00Z Content type: article Language: en Sources: [InfoQ]() Topics: [Large Language Model](), [Infrastructure as code](), [Retrieval Augmented Generation (RAG)](), [Code](), [Fable]() Tags: [ai](), [ai-ml-data-engineering](), [article](), [artificial-intelligence](), [benchmarks](), [development](), [domain-specific-languages](), [dsls](), [hallucinations](), [infrastructure-as-code](), [large-language-model](), [large-language-models](), [ml-data-engineering](), [next-dsl-author-language-model](), [validation]() ### AI overview The article introduces Typed Domain Grounding (TDG), which embeds a domain-specific language as a typed internal DSL in a mainstream host language. It argues that compiler type errors and generate-compile-repair loops can reduce syntactic hallucinations in language-model output. A benchmark reported higher Structural Fidelity and lower hallucination rates than two lenient external DSLs, although first-try compile rates were lower and results varied by model. ### Source excerpt In this article, the author introduces Typed Domain Grounding, an approach to reducing LLM hallucinations in domain-specific languages by embedding them in mainstream typed languages. Using kUML benchmarks and an infrastructure-as-code example, he explores how compiler validation and generate-compile-repair loops can make model-generated DSL output more reliable. By Irakli Betchvaia ## 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. ## OpenSearch Wins Analytics & Data Intelligence Solutions Category in the SiliconANGLE TechForward Awards DevFeed: [OpenSearch Wins Analytics & Data Intelligence Solutions Category in the SiliconANGLE TechForward Awards]() Original publisher: [Read original article]() Author: Kristi Piechnik Published: 2026-09-14T12:00:14Z Content type: news Language: en Sources: [OpenSearch]() Topics: [Open Source](), [AI Infrastructure](), [observability](), [AI search](), [Security](), [Retrieval Augmented Generation (RAG)](), [Databases](), [real-time]() Tags: [2026](), [agentic-ai](), [ai-agents](), [ai-infrastructure](), [announcements](), [awards](), [data](), [enterprise](), [observability](), [open-source](), [opensearch](), [real-time](), [recognition](), [retrieval](), [retrieval-augmented-generation-rag](), [search](), [security]() ### AI overview OpenSearch won the Analytics & Data Intelligence Solutions category in SiliconANGLE Media's 2026 TechForward Awards. The recognition highlights its open source, vendor-neutral platform for enterprise search, observability, security analytics, vector databases, and agentic AI workloads. ### Source excerpt Recognition validates open source momentum, architectural consolidation, and enterprise scale as the project marks five years of community growth The post OpenSearch Wins Analytics & Data Intelligence Solutions Category in the SiliconANGLE TechForward Awards appeared first on OpenSearch. ## 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 ## 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. ## CERN PGDay 2027: Announcement and CfP DevFeed: [CERN PGDay 2027: Announcement and CfP]() Original publisher: [Read original article]() Author: Swiss PostgreSQL Users Group Published: 2026-09-11T00:00:00Z Content type: news Language: en Sources: [PostgreSQL news]() Topics: [Databases](), [Machine learning](), [AI search](), [Retrieval Augmented Generation (RAG)]() Tags: [announcement](), [community](), [conference](), [database](), [postgresql]() ### AI overview CERN PGDay 2027 is an announcement and call for papers for a single-day PostgreSQL community conference at CERN in Geneva. Proposed talk themes include large-scale database performance, AI and vector-search workloads, security, and PostgreSQL extensibility. ### Source excerpt CERN PGDay 2027 Date: Friday, February 12, 2027 Location: CERN Campus, Geneva, Switzerland / Suisse Romande Language: English Organizers: Swiss PostgreSQL Users Group (SwissPUG) & CERN Format: Single-track (6-7 sessions) followed by networking social event Homepage: swisspug.org/cern-pgday-2027 About the Event Continuing in the line of work of the past editions, CERN PGDay 2027 returns as the annual gathering for PostgreSQL users, developers, and enthusiasts across Suisse Romande (western Switzerland) and the neighboring French border regions. Co-organized by CERN and SwissPUG, this single-day community conference offers a premier opportunity to network, share practical experiences, and explore the future of the world's most advanced open-source database. The event takes place in the unique, international environment of Geneva--a hub for major scientific institutions, non-governmental organizations, and financial and tech enterprises. Format & Venue Single-Track Schedule: The program consists of a single track featuring 6 to 7 technical sessions presented entirely in English. Social Event & Networking: Following the technical presentations, a social event will take place to facilitate community networking, discussions, and collaboration in the inspiring atmosphere of CERN. CERN Visits: Attendees are encouraged to arrange their schedules before or after the event to take advantage of the conference location and visit CERN, the European Organization for Nuclear Research. Call for Papers (CfP) Speaker proposals for CERN PGDay 2027 can be entered online via Indico. Call for Papers Closes: November 8, 2026 (23:59 CET) Call for Sponsors (CfS) Sponsors proposals for CERN PGDay 2027 are welcome. Please find the contract with all details online. Key Themes We welcome talk proposals covering broader PostgreSQL and community trends and localized enterprise & scientific topics: High-Performance Science & Big Data: Managing massive datasets, extreme write throughput, partitioning ## 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. ## 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. ## 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. ## Java Weekly, Issue 660 DevFeed: [Java Weekly, Issue 660]() Original publisher: [Read original article]() Author: baeldung Published: 2026-08-22T11:26:18Z Content type: article Language: en Sources: [Baeldung]() Topics: [API](), [JSON](), [Quarkus](), [Gradle](), [Retrieval Augmented Generation (RAG)](), [Amazon Bedrock](), [agent observability]() Tags: [amazon-bedrock](), [api](), [gradle](), [java](), [json](), [no-ads](), [no-after-post](), [no-before-post](), [no-optins](), [rag](), [release](), [weekly-review](), [weekly-review-no-ads-no-after-post-no-before-post-no-optins]() ### AI overview Java Weekly, Issue 660 is a roundup of Java ecosystem news, including a Hibernate second-level cache performance case study, a proposed small JSON API for JDK 28, Gradle and Renovate integration, AI and RAG resources, and recent Quarkus and other library releases. ### Source excerpt A clear Hibernate speedup and a new JSON API coming to Java. The post Java Weekly, Issue 660 first appeared on Baeldung. ## Beyond Relevance: Building a Quality-Aware Retrieval Layer for RAG DevFeed: [Beyond Relevance: Building a Quality-Aware Retrieval Layer for RAG]() Original publisher: [Read original article]() Author: Shay Ben-Elazar Published: 2026-08-18T07:16:01Z Content type: article Language: en Sources: [Data Science at Microsoft]() Topics: [Retrieval Augmented Generation (RAG)](), [Large Language Model](), [Artificial Intelligence]() Tags: [ai](), [data-science](), [education](), [machine-learning](), [microsoft](), [quality](), [rag](), [retrieval]() ### AI overview The article examines how a metadata-aware quality layer could improve educational RAG systems by ranking or filtering retrieved sources for reliability, clarity, evidence, and suitability for teaching. It argues that relevance alone does not ensure educational quality and that filtering weak sources can also reduce context noise, token use, and inference costs. ### Source excerpt How a metadata-aware quality layer can help educational RAG systems retrieve sources that are relevant, reliable, and ready for teaching Shay Ben-Elazar, Principal Applied Data Science Manager, Microsoft Rob Mauceri, Distinguished Engineer, Microsoft A student asks an AI tutor a science question: "Why do flu vaccines need to be updated over time?" A retrieval-augmented generation (RAG) system searches its index, retrieves passages about influenza viruses, vaccine development, immunity, and seasonal outbreaks, then generates an answer grounded in those sources [1]. At first glance, the system appears to have done its job. The passages are relevant, and the answer cites supporting information. But a deeper question remains: were the retrieved sources actually good sources to learn from? Some passages may be outdated, overly technical, weakly supported, or poorly organized for a student audience. Others may provide clearer explanations, stronger evidence, and a more coherent path to understanding. Both may be relevant, yet only one is likely to help a learner build accurate understanding [2]. This distinction matters because relevance alone is not the same as educational quality. Retrieved passages can match the topic while lacking clear definitions, supporting evidence, or an easy-to-follow explanation. In a student-facing product, those differences shape not only what the student learns, but also how much they trust the system. There is also a practical systems reason to care. Web-scale RAG applications operate within limited context windows and real compute budgets. Filtering weak sources earlier, or downranking them before generation, reduces noise, preserves tokens for stronger evidence, and lowers inference costs, compounding across products used by millions of learners [3]. RAG has become one of the most practical ways to make large language model applications more grounded. It can make answers more current, more domain-specific, and easier to connect back to so [Next page]()