Items tagged with “RAG”.
Verisk Rating Insights, powered by Amazon Bedrock, LLMs, and RAG, enables a conversational interface to access ISO ERC changes, reducing manual downloads and enabling faster, accurate insights.
Streamline experimentation to production for Retrieval Augmented Generation (RAG) with SageMaker AI, MLflow, and Pipelines, enabling reproducible, scalable, and governance-ready workflows.
Explains token-level confidence data for imported Bedrock models via log probabilities, how to enable, and practical use cases for reliability.
NVIDIA NIM Operator 3.0.0 expands scalable AI inference on Kubernetes, enabling multi-LLM and multi-node deployments, KServe integration, and DRA support in technology preview, with Red Hat collaboration and NeMo Guardrails.
Automate deploying Amazon Bedrock knowledge bases and data-source connections for RAG workflows with a Terraform IaC template, enabling rapid, reproducible setups for production systems.
Amazon Finance details an AI-powered assistant that blends Bedrock and Kendra to accelerate data discovery, preserve institutional knowledge, and deliver accurate financial insights at scale.
A new agentic AI assistant for Buenos Aires, built with LangGraph and Amazon Bedrock, uses guardrails and a reasoning retrieval system to answer citizens’ questions about government procedures while ensuring safe, accurate responses.
Explores how to integrate geospatial data and GIS workflows with Amazon Bedrock, leveraging LLMs, RAG, and Bedrock Agents to unlock insights and streamline operations.
An end-to-end demonstration of hyper-personalized consumer goods and advertising campaigns built with Amazon Nova in Bedrock, showcased at Cannes Lions 2025 and backed by Guardrails, RAG, and on-site perfumers.
We built an advanced RAG solution using Amazon Bedrock leveraging Infosys Topaz™ AI capabilities, tailored for the oil and gas sector. This solution excels in handling multimodal data sources, seamlessly processing text, diagrams, and numerical data while maintaining context and relationships betwee
Amazon Q Business integrates Agentic Retrieval Augmented Generation (RAG) to transform enterprise data interaction with decomposed queries, real-time processing visibility, and memory-enabled conversations, grounded in enterprise data with clear citations.
A deep dive into In-Context Retrieval and Reasoning (ICR2) for long-context LLMs, including benchmarks, methods, and implications for retrieval-augmented generation (RAG).
NVIDIA vGPU 19.0 takes full advantage of the RTX PRO Blackwell architecture, enabling MIG-enabled graphics and AI virtualization with up to 48 concurrent VMs per GPU and new tools for AI workflows in virtualized data centers.
TinyAgent shows small language models can be fine-tuned for reliable function calling and edge deployment, using curated synthetic data, an LLMCompiler planner, and a Tool RAG approach to power private, low-latency agentic workflows.
New work demonstrates vec2text can recover substantial input from text embeddings, challenging assumptions that embeddings are a secure, lossy representation.
Examines whether text embeddings can be inverted to recover original text, detailing vec2text and its impact on privacy in embedding-based systems.