Generate presentations with Amazon Bedrock AgentCore
AWS outlines how to build an agent system on Amazon Bedrock AgentCore that generates grounded PowerPoint presentations from enterprise documents in under 90 seconds.
#rag
29 articles tagged with “rag”.
Rethinking access control for RAG with Amazon Quick and Amazon Bedrock
AWS outlines how Amazon Quick and Amazon Bedrock Knowledge Bases enforce document-level access controls for RAG, checking permissions with authoritative sources in real time.
How Qlik built grounded, enterprise-scale AI with Amazon Bedrock
Qlik built Qlik Answers on Amazon Bedrock to deliver grounded, sourced answers across structured and unstructured enterprise data for its 40,000+ customers.
Building a context-aware AI assistant on AgentCore and OpenClaw
An AWS tutorial explains how to build a personal AI assistant that retains context across conversations using OpenClaw on Amazon Bedrock AgentCore and its memory feature.
Fine-tune a search agent with multi-turn RL on Amazon SageMaker AI
AWS describes fine-tuning a small LLM search agent using multi-turn reinforcement learning on Amazon SageMaker AI, aiming for frontier-model reliability at lower latency and cost.
Query claims in natural language with Amazon Bedrock Knowledge Bases
AWS details how to build a claims assistant on Amazon Bedrock Knowledge Bases that answers natural-language questions with citations, using document ingestion, retrieval APIs, and guardrails.
The generative AI customization spectrum: From prompt engineering to custom models on AWS
AWS presents a decision framework for selecting generative AI customization methods, spanning prompt engineering, RAG, fine-tuning, continued pre-training, and Amazon Nova Forge.
Customizing your knowledge base on Amazon Bedrock for large and complex documents using Amazon Textract
A guide to customizing Amazon Bedrock knowledge bases for large, complex documents by pairing Amazon Textract text extraction with generative AI to query documents like utility bills at scale.
Modernizing and scaling support operations with generative AI on AWS
AWS describes building a generative AI support operations platform that converts training videos into structured SOPs, applies RAG to guide ticket resolution, and predicts SLA risk to prioritize work.
Governed reports with Amazon Quick Desktop and Amazon FSx for NetApp ONTAP
AWS outlines a governed weekly reporting workflow combining Amazon Quick Desktop and Amazon FSx for NetApp ONTAP, drafting cited reports and Slack summaries with human review before sharing.
Democratizing institutional knowledge: Building an AI-powered knowledge management system with AWS
AWS describes how to build a customizable knowledge management system that delivers institutional knowledge via a voice-first AI avatar, using Amazon Bedrock Knowledge Bases for RAG.
Reduce RAG costs on Amazon Bedrock with query-aware compression
AWS describes a query-aware compression approach on Amazon Bedrock that uses a smaller model to filter retrieved chunks before answering, reducing input tokens and cost in RAG.
AWS vector solutions: Build agentic AI where your data lives
An AWS post outlines vector search capabilities integrated into its existing databases and storage services, covering six purpose-built services and guidance for selecting an engine.
VentureBeat names Rob Strechay as its first Lead Analyst, expanding its enterprise AI research push
VentureBeat has appointed Rob Strechay, formerly of theCUBE Research, as its first Lead Analyst and founding analyst of VentureBeat Research, deepening its enterprise AI coverage.
Improve contract search accuracy with auto-generated filters in Amazon Bedrock
An AWS post explains how AIDA applies implicit and explicit filtering plus metadata-enriched chunking in Amazon Bedrock Knowledge Bases to improve contract search accuracy.
How OneAdvanced deployed over 50 AI agents on UK-sovereign AWS
UK software provider OneAdvanced deployed over 50 AI agents on a UK-sovereign AWS platform, self-hosting Llama 4 Maverick and Llama Guard 4 with a pgvector RAG pipeline.
Automated web insight extraction with Amazon Bedrock AgentCore
AWS details an automated pipeline that monitors RSS feeds, renders web pages, and makes AI-extracted insights searchable using Amazon Bedrock AgentCore and related services.
Beyond RAG: Task-aware knowledge compression for enterprise AI on AWS
An AWS blog post presents task-aware knowledge compression (TAKC), which pre-compresses knowledge bases into task-specific representations across fidelity tiers to handle analytical tasks spanning many documents.
Building trade assistant: How Jefferies optimized front office trading operations with AI
Jefferies deployed an AI-powered trade assistant built on Strands Agents, Amazon Bedrock, and the Model Context Protocol to improve front office trading operations.
Agentic retrieval for Amazon Bedrock Managed Knowledge Base
AWS explains agentic retrieval for Amazon Bedrock Managed Knowledge Base, covering the AgenticRetrieveStream API and when to choose it over the standard Retrieve API.
The AI context gap: Enterprise AI organizations have a trust problem, not a retrieval problem — and mo…
A survey of 101 enterprises finds AI agents often produce confident but wrong answers traced to missing or inconsistent business context, exposing a trust gap in enterprise RAG infrastructure.
NVIDIA Nemotron 3 Embed Ranks #1 Overall on RTEB, Advancing Agentic Retrieval
NVIDIA's Nemotron 3 Embed model has taken the top overall spot on the RTEB benchmark, positioned around advancing agentic retrieval.
What Is a Vector Database, and When Do You Need One?
A vector database stores embeddings and finds the most similar ones fast. Here is what it actually does, how nearest-neighbor search works, and when you need one versus a simpler option.
How to Design a RAG System (System Design, Explained)
Designing a RAG system is mostly a search problem with a language model bolted on the end. Here is how the pieces fit together in production — and the design choices that decide whether it works.
How to Actually Evaluate an LLM App (Without Fooling Yourself)
A demo that works on five hand-picked prompts is not a working AI product. Here is how to measure LLM quality honestly — the metrics, the judge models, and the datasets — so you catch failures before your users do.
What Is RAG? Retrieval-Augmented Generation Explained Simply
RAG is the technique that lets a language model answer using your own documents instead of only what it memorized in training. Here is how it works and why almost every serious AI app uses it.
What Are Embeddings and Vector Search? A Plain-English Guide
Embeddings are how AI turns words into numbers that capture meaning — the quiet engine behind semantic search, recommendations, and RAG. Here is the concept without the math.
Perplexity: The AI Answer Engine Challenging Traditional Search
Perplexity answers questions with concise, cited summaries drawn from the live web — positioning itself between a search engine and a chatbot. Here is what it does and where it fits.
LangChain: The Framework That Defined AI Application Orchestration
LangChain is the open-source framework that popularized chaining LLM calls, retrieval, and tools into full applications. Here is what it offers, and the trade-offs to weigh before building on it.