搜索结果：llms.txt

LLMs.txt files act as AI tour guides for websites, directing AI systems to valuable content and preventing misrepresentation. Our generator creates optimized files in minutes through deep crawling, NL

This comprehensive guide explains Retrieval-Augmented Generation (RAG) as a solution to large language models' knowledge cutoff and hallucination issues, detailing its five-generation evolution, pract

DeepGEMM is an open-source high-performance tensor core kernel library from DeepSeek, focusing on optimizing GEMM operations for large language models with efficient FP8 precision and fine-grained sca

Unstract is an open-source platform that uses LLMs to extract structured JSON data from various document formats (PDFs, images, scans) through natural language prompts, deployable as APIs or ETL pipelines for finance, insurance, healthcare, and compliance teams. 原文翻译： Unstract是一个开源平台，利用大语言模型通过自然语言提示从各种文档格式（PDF、图像、扫描件）中提取结构化JSON数据，可作为API或ETL管道部署，适用于金融、保险、医疗和合规团队。

This guide explores how Large Language Models (LLMs) can accelerate GPU kernel optimization, bridging the gap between research algorithms and production-ready implementations. It covers the computational foundations of matrix multiplication, the mathematical framework from Strassen to tensor decomposition, and DeepMind's evolution from AlphaTensor to AlphaEvolve, providing a technical roadmap for self-improving AI infrastructure. 原文翻译： 本指南探讨了大型语言模型（LLM）如何加速GPU内核优化，弥合研究算法与生产就绪实现之间的差距。它涵盖了矩阵乘法的计算基础、从Strassen到张量分解的数学框架，以及DeepMind从AlphaTensor到AlphaEvolve的演进历程，为自改进AI基础设施提供了技术路线图。

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。

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。

RAG-Anything is an all-in-one RAG framework developed by HKUDS at the University of Hong Kong, open-sourced on GitHub. It aims to enhance LLM performance by integrating retrieval and generation, addre

This article provides a comprehensive comparison and ranking of over 100 AI models (LLMs) across key metrics including intelligence, price, performance, speed (tokens per second & latency), and context window size. It identifies top performers in each category, such as Gemini 3.1 Pro Preview and GPT-5.4 (xhigh) for intelligence, Mercury 2 and Granite 3.3 8B for speed, and Qwen3.5 0.8B for affordability. 原文翻译： 本文对100多款AI大模型（LLMs）在智能、价格、性能、速度（每秒令牌数及延迟）和上下文窗口大小等关键指标上进行了全面比较和排名。文章识别了各分类中的顶级模型，例如Gemini 3.1 Pro Preview和GPT-5.4 (xhigh)在智能方面表现最佳，Mercury 2和Granite 3.3 8B速度最快，Qwen3.5 0.8B最具性价比。

Retrieval-Augmented Generation (RAG) is an AI framework that enhances Large Language Models (LLMs) by integrating real-time information retrieval from external knowledge bases, addressing limitations