rule-based approaches fall short. Consider the example of payment fraud analysis. A traditional rules engine works like a checklist, flagging transactions based on preset criteria. In contrast, an LLM agent Examples Data Enable agents to retrieve context and information necessary for executing the workflow. Query transaction databases or systems like CRMs, read PDF documents, or search the web. Action Enable instructions executes workflows in a loop 02 Multi-agent systems, where workflow execution is distributed across multiple coordinated agents Let’s explore each pattern in detail. 13 A practical guide

Telegraph Electrification Mass Steel Production Mass Production & Assembly Lines Internal Combustion Engine Flight Synthetic Fertilizer Transistors PCs Internet Smartphones Cloud12 …Technology Compounding to computing, calculating or counting patents. Google patents data changes somewhat between each query so numbers are rounded and should be viewed as directionally accurate. Source: USA Patent & Trademark deployment frameworks that accompany it. The productivity upside may be significant, but unevenly distributed. The geopolitical, ethical, and economic implications may evolve gradually, not abruptly. As

approaches have been explored to address this issue, including Grouped-Query Attention (GQA) (Ainslie et al., 2023) and Multi-Query Attention (MQA) (Shazeer, 2019). However, these methods often compromise limit the inference efficiency. In order to reduce the KV cache, Multi-Query Atten- tion (MQA) (Shazeer, 2019) and Grouped-Query Attention (GQA) (Ainslie et al., 2023) are proposed. They require a smaller respectively: q? = ??h?, (1) k? = ? ?h?, (2) v? = ??h?, (3) 6 Grouped-Query Attention (GQA) Multi-Head Attention (MHA) Multi-Query Attention (MQA) Multi-Head Latent Attention (MLA) Keys Queries Values

can face while crafting prompts. Prompt engineering Remember how an LLM works; it’s a prediction engine. The model takes sequential text as an input and then predicts what the following token should be specific aspects of the RAG system that impact what content was inserted into the prompt, including the query, chunk settings, chunk output, and other information. Once you feel the prompt is close to perfect

rights reserved. Amazon/Intel Confidentia Presenter: Zhi Chen, Cody Yu Amazon SageMaker Neo, Deep Engine Science Bring Your Own Codegen to TVM AWS AI© 2019, Amazon Web Services, Inc. or its Affiliates Implement extern operator functions, OR 2. Implement a graph annotator Generate binary/library/engine for the subgraph ● Implement an IR visitor for codegen ● Implement the build logic© 2019, Amazon Implement the Codegen ● Implement a codegen class to accept subgraphs and build binary/library/engine for runtime dispatching ● Codegen path: src/relay/backend/contrib//codegen.cc

开发工具有： ：帮助用户极致优化 给大模型的提示词（prompt），使得对大语 言模型提问时，可以获得更理想的输出。 ：用于语义搜索、LLM 编排和语言模 型工作流的一体化嵌入数据库，可以使用 SQL、对象存储、主题建模、图形分析和多模 态索引进行矢量搜索。 ：专注以 Sketch、PSD、静态 图片等形式的视觉稿作为输入，通过智能化技 术一键生成可维护的前端代码，包含视图代码、 数据字段绑定、组件代码、部分业务逻辑代码。

JetBrains AI AI 编程开发助手，集成在 JetBrains 系列开发工具中，提升编码效率。 9 Figure 6: AI 编程工具 2.4.3 AirOps 用于生成和修改 SQL 语句的工具，旨在简化数据库操作。 2.4.4 ChatDev 面壁智能开发的 AI 智能体开发平台，支持创建和部署智能对话系统。 2.4.5 solo Mozilla 开源项目，提供零代码网站开发功能，易于使用。

snails distributed across a vertical rocky intertidal gradient. Functional Ecology 25:177-185 Bourdeau PE(2011) Constitutive and inducible defensive traits in co-occurring marine snails distributed across

FacelD Multimodal Interection CPU (ARM、Intel) 1驱动万物智能 Accelerated Op Library / Others Inference Engine DSP (Qualcomm) PART TWO Alios TVM @ ARM CPU AiOS 1驱动万物智能 Alios TVMQOARM CPU 。 Support TFLite 1024 1024, 1024 PART Five Misc AiOS 1驱动万物智能 M Nvidia GTX 1050 。， Integrate other inference engine (like TRT) 2 _ _ 10 9

its Affiliates. All rights reserved. Presenter: Haichen Shen, Yao Wang Amazon SageMaker Neo, Deep Engine Science Dynamic Model in TVM AWS AI© 2019, Amazon Web Services, Inc. or its Affiliates. All rights