Knowledge catalogue
Enterprise RAG unlocks insights from knowledge sources like SharePoint, Google Drive, and Confluence, but those sources carry complex permissions. Learn how Amazon Quick and Amazon Bedrock Knowledge B
Input tokens are often a meaningful part of the cost of running Retrieval Augmented Generation (RAG) at scale. This post describes a query-aware context compression pattern on Amazon Bedrock: after re
Reducing Retrieval and Calls When a retrieval-augmented generation (RAG) agent runs, it often has to plan a search, check the evidence it gets back, and try again when that evidence falls short. Those
Traditional RAG hits a ceiling on analytical tasks that span hundreds of documents. This post shows how to use task-aware knowledge compression (TAKC) on AWS to pre-compress entire knowledge bases int
Retrieval Augmented Generation (RAG) is a technique that combines information retrieval with generative AI to enhance model outputs by fetching relevant external documents or data before generating re
Google's RAG Engine securely connects private enterprise data to LLMs to improve answer accuracy and reduce hallucinations , making it a key component of the Gemini Enterprise Agent Platform for build
This guide demonstrates how to build a coding assistant using Weaviate's Model Context Protocol (MCP) integration, enabling retrieval-augmented generation (RAG) capabilities over codebases and documen
When building consumer-facing generative AI applications, balancing high generation quality with fast response times across diverse media types, can be challenging. KDDI, a major telecommunications ca
Executive Summary Since the release of our May 2026 report detailing adversarial misuse of artificial intelligence (AI), Google Threat Intelligence Group (GTIG) has observed forward leaning adversarie
Benchmarking Retrieval-Augmented Generation End to End — from Building the Vector Database to Serving an Iterative, Multi-Hop Question-Answering Inference Pipeline The post Introducing the MLPerf End-
In this post, we demonstrate how to implement HippoRAG using a comprehensive AWS stack. We use Amazon Bedrock for LLM capabilities, Amazon Neptune for graph database functionality, Amazon Neptune Anal
Auto-generated index of all tools mentioned across the wiki.
Auto-generated index of all companies mentioned across the wiki.
Auto-generated index of all models mentioned across the wiki.
Auto-generated index of all papers mentioned across the wiki.
Automated lint: 17 errors, 59669 warnings, 3 info
Automated lint: 14 errors, 54840 warnings, 3 info
Automated lint: 12 errors, 54839 warnings, 3 info
Automated lint: 12 errors, 53990 warnings, 3 info
Automated lint: 19 errors, 64915 warnings, 3 info
Auto-generated synthesis of 29846 entries about arxiv-cs-ai
Auto-generated synthesis of 10865 entries about arxiv-cs-cl
Automated lint: 26 errors, 6728 warnings, 3 info
Retrieval-augmented generation (RAG) enhances large language models (LLMs) with external knowledge but still suffers from long contexts and disjoint retrieval–generation optimization. In this work, we
Choosing the right vector store for your Amazon Bedrock Knowledge Bases RAG application affects performance and cost. This post compares Amazon OpenSearch Service, Amazon Aurora PostgreSQL with pgvect
Vector search is a critical component of generative AI, retrieval-augmented generation (RAG), and data agent architectures, but sometimes vector search alone isn't enough. While vector embeddings are
This week we kicked off the DevFest season in North America at Google Hudson Square in New York City with 80 engineers packed into the room. Typical technical workshops hand you a finished repo, tell
Enterprise content management is experiencing its biggest architectural shift since the cloud migration era. For years, enterprises have stored trillions of gigabytes of critical data in Box: financia
Automated lint: 20 errors, 8743 warnings, 3 info
For modern AI and RAG applications, achieving high search relevance requires that you combine at least two techniques: vector search for semantic context, and full-text search (FTS) for keyword precis
Co-authors:Stenal Jolly, Strategic Cloud Engineer, GoogleAnubhav Dhawan, Software Engineer, Google Following the landmark announcement of MCP Toolbox v1.0, we're thrilled to announce that the MCP Tool
Build a Retrieval Augmented Generation (RAG) application on Amazon Bedrock Managed Knowledge Base with LangChain, and see how agentic retrieval handles the multi-part questions that single-shot retrie
Manually extracting data from hundreds of vendor contracts doesn't scale, and RAG chat tools fall short on portfolio-wide questions. This post shares a contract intelligence platform on AWS that uses
As query traffic grows, so does the compute bill for embedding it. On a low-power device, a large model may not fit in memory. A smaller query model could reduce that cost, but switching usually means
As threat actors increasingly use AI to accelerate and develop cyberattacks, enterprise defenders need to rely on both AI and a critical defender’s advantage: Business context that only you possess. E
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 simpl
Learn how OneAdvanced, a UK enterprise software provider, built a UK-sovereign AI platform by self-hosting Llama 4 Maverick and Llama Guard 4 on Amazon SageMaker AI, with a RAG pipeline on pgvector an
The Ettin Reranker Family represents a new suite of reranking models released on Hugging Face designed to improve retrieval-augmented generation (RAG) systems by reordering search results based on rel
This article explains how retrieval quality directly impacts large language model performance in retrieval-augmented generation (RAG) systems, emphasizing that even advanced LLMs cannot produce better
Inception has launched Mercury 2, a model providing reasoning-grade quality with low, real-time latency, making it suitable for complex applications like agentic loops, coding assistants, and RAG p...
On-device personal AI requires models and agent software that can operate within fixed hardware limits. Developers also need ways to keep those systems improving after deployment. Model builders are r
This technical how-to builds a conversational claims assistant on Amazon Bedrock Knowledge Bases that answers natural-language questions with citations. It covers ingesting claim documents from Amazon
Amazon Bedrock has released OpenAI’s GPT‑6 Sol and GPT‑6 Luna as generally available services, offering distinct capabilities for advanced reasoning/coding (Sol) and high‑volume repeatable tasks (Luna
Latest version of the benchmark introduces two new tests for emerging AI deployment patterns, including Agentic Inference The post MLCommons Sets Participation Record with New MLPerf Inference v6.1 Be
As enterprise organizations scale mission-critical applications, storage I/O and memory access can become severe operational bottlenecks. Whether its Oracle databases, in-memory databases like SAP HAN
Prompt caching in Amazon Bedrock can cut input token costs by up to 90% when you repeatedly send the same context to foundation models. This post walks through six practical prompt caching scenarios u
Modern enterprises possess a vast amount of unstructured data, yet they frequently encounter significant challenges in managing and extracting value from it. Historically, unlocking the insights hidde
If you’re an IT leader, you might be getting a lot of questions about how to build and deploy agents. The pressure to move fast is intense, but the engineering reality is incredibly complex. Where do
In the agentic era, the role of the database has fundamentally changed. It is no longer a passive repository; it’s a critical context engine designed to ground generative AI apps, models and power aut
As generative AI moves from experimental pilots to massive production environments, the efficiency of your infrastructure becomes the ultimate differentiator. One way to get the most out of it and min
Deploying large language models (LLMs) in real-world, high-stakes settings is harder than it should be. In high-stakes settings like law, medicine, and cloud incident response, performance and reliabi
Over 80% of enterprise data lives in unstructured form — PDFs, emails, reports, regulatory filings. Most of the time, such sources contain critical business information, yet they remain difficult to a
Agents are only as capable as the context they can reach. An agent with reliable access to the right information makes decisions grounded in fact; one without it guesses. And the work agents produce i
NVIDIA NemoClaw delivers a memory‑driven Chief of Staff agent that constructs a structured self‑model (people, projects, priorities) stored in Markdown pages, with an SQLite ledger recording obligatio
It’s never been easier to start an AI-powered startup on Google Cloud. You grab an API key from Google AI Studio at breakfast, paste it into Antigravity, and by lunch you’ll have a nascent prototype o
Rob Strechay, until recently managing director and principal analyst at theCUBE Research, has joined VentureBeat as our first Lead Analyst and a founding analyst of VentureBeat Research. His arrival i
In today’s retail environment, shoppers expect highly personalized product discovery experiences and conversational assistance that feels genuine, natural, and genuinely helpful. Today, successful pro
DigitalOcean launched Kimi K3 on day 0. It’s already one of the most popular models on the platform and across the market: second most likes on Hugging Face, sixth most traffic on OpenCode. Getting a
AlloyDB is a fully managed, PostgreSQL-compatible database service built for your most demanding enterprise workloads. It combines the best of open source PostgreSQL with Google’s advanced technology,
How should your security team manage shadow AI? Workloads deployed by developers without formal registration can often evade traditional security scanners, because organizations are reluctant to slow