搜索结果：结构化数据

This article explores how large language models (LLMs) function as document completion engines and demonstrates how to build practical applications by mapping between user and document domains, using GitHub Copilot's prompt engineering pipeline as a detailed case study. 原文翻译： 本文探讨了大型语言模型（LLM）如何作为文档完成引擎工作，并通过在用户领域和文档领域之间建立映射来构建实际应用，以GitHub Copilot的提示工程流程作为详细案例研究。

BlaBlaBlAI is an open-source chat system that enables multiple LLMs and humans to interact in the same room with full awareness of all participants and complete chat history, featuring cost attribution and flexible chat creation. 原文翻译： BlaBlaBlAI是一个开源聊天系统，允许多个LLM和人类在同一个房间内互动，所有参与者都能完全感知彼此并查看完整的聊天历史，具有成本归因和灵活的聊天创建功能。

This comprehensive 2026 ranking analyzes major LLMs across reasoning, coding, math, agentic, software engineering, and chat benchmarks, providing detailed performance scores and pricing comparisons for technical professionals. 原文翻译： 这份全面的2026年排名分析了主要大语言模型在推理、编码、数学、代理、软件工程和聊天基准测试中的表现，为技术专业人士提供详细的性能得分和定价对比。

EDDI (Enhanced Dialog Driven Interface) is a production-grade, config-driven multi-agent orchestration middleware for conversational AI. It coordinates users, AI agents, and business systems through intelligent routing, persistent memory, and API orchestration — without writing code. 原文翻译： EDDI（增强型对话驱动接口）是一个生产级、配置驱动的多智能体编排中间件，用于对话式AI。它通过智能路由、持久化内存和API编排来协调用户、AI智能体和业务系统——无需编写代码。

Leading AI researchers identify four key innovations—steering, memory, tool integration, and multimodality—that will transform LLM capabilities over the next 6-12 months, enabling more reliable, personalized, and actionable AI applications for both enterprise and consumer use cases. 原文翻译： 顶尖AI研究人员确定了四大关键创新——控制、记忆、工具集成和多模态——这些将在未来6-12个月内改变大语言模型的能力，为企业和消费者应用场景提供更可靠、个性化和可操作的AI解决方案。

NVIDIA H100 Tensor Core GPUs set new records across all eight MLPerf training benchmarks, delivering exceptional performance for generative AI and large language models at both per-accelerator and massive scale configurations. 原文翻译： NVIDIA H100 Tensor Core GPU在MLPerf训练基准测试的所有八项测试中均创下新纪录，在单加速器和大规模配置下均能为生成式AI和大语言模型提供卓越性能。

The OpenAI Agents SDK is a lightweight, provider-agnostic Python framework for building multi-agent workflows with features like sandbox agents, tools, guardrails, and real-time voice support. 原文翻译： OpenAI Agents SDK 是一个轻量级、提供商无关的 Python 框架，用于构建具有沙盒代理、工具、护栏和实时语音支持等功能的多智能体工作流。

Ragas is a comprehensive Python toolkit for evaluating and optimizing Large Language Model (LLM) applications with objective metrics, automated test generation, and seamless integrations with popular frameworks like LangChain. 原文翻译： Ragas 是一个全面的 Python 工具包，用于通过客观指标、自动化测试生成以及与 LangChain 等流行框架的无缝集成来评估和优化大型语言模型（LLM）应用。

This paper provides a comprehensive systematic review of Retrieval-Augmented Generation (RAG), tracing its evolution from early open-domain QA to current state-of-the-art implementations, analyzing core components, deployment challenges, and emerging solutions for more reliable knowledge-intensive NLP systems. 原文翻译： 本文对检索增强生成（RAG）进行了全面的系统综述，追溯了其从早期开放域问答到当前最先进实现的发展历程，分析了核心组件、部署挑战以及为更可靠的知识密集型NLP系统而出现的新兴解决方案。

Together AI introduces ATLAS, an adaptive-learning speculator system that dynamically improves LLM inference performance at runtime, achieving up to 4x faster decoding speeds without manual tuning. 原文翻译： Together AI推出ATLAS自适应学习推测系统，该系统在运行时动态提升大语言模型推理性能，无需手动调优即可实现高达4倍的解码加速。