scientist or a machine learning engineer – everyone can write a prompt. However, crafting the most effective prompt can be complicated. Many aspects of your prompt affect its efficacy: the model you use, documentation or reasoning. Please feel free to refer to Google’s prompting guides2,3 with simple and effective prompting examples. When prompt engineering, you will start by choosing a model. Prompts might configuration. Most LLMs come with various configuration options that control the LLM’s output. Effective prompt engineering requires setting these configurations optimally for your task. Output length

This document is filled with user, usage and revenue charts that go up-and-to-the-right… often supported by spending charts that also go up-and-to-the right. Creators / bettors / consumers are taking AI & Physical World Ramps = Fast + Data-Driven • Global Internet User Ramps Powered by AI from Get-Go = Growth We Have Not Seen Likes of Before • AI & Work Evolution = Real + Rapid 3 1 2 3 4 5 Operating Zone Market Share Source: YipitData (4/25) Global Internet User Ramps Powered by AI from Get-Go = Growth We Have Not Seen Likes of Before 7 Leading USA-Based LLM App Users by Region Note: Region

handoffs between agents but allow the model to run multiple steps until an exit condition is met. An effective strategy for managing complexity without switching to a multi-agent framework is to use prompt handoff to the order_management_agent, transferring control to it. This pattern is especially effective for scenarios like conversation triage, or whenever you prefer specialized agents to fully take additional ones as you uncover new vulnerabilities. We’ve found the following heuristic to be effective: 01 Focus on data privacy and content safety 02 Add new guardrails based on real-world edge cases

started Morgan Stanley’s first eval focused on making their financial advisors more efficient and effective. The premise was simple: If advisors could access information faster and reduce the time spent

apps/vta_rpc/start_rpc_server.sh Step 8: Configure vta/config/de10nano_config.json to vta_config.json Step 9: Go to vta/hardware/intel and run make command Step 10: Get the generated .sof file programmed into hardware

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SubclassesUnified Runtime Benefit mod.export_library("mylib.so") Unified library packaging Free API (Py/Java/Go) lib = tvm.module.load("mylib.so") func = lib["npufunction0"] func(a, b) Automatic RPC Support

28 例：课后作业 仔细思考政企、创业者必读 DeepSeek-R1是AI发展史上的重要里程碑 R1形成了新的AGI定律，加速了AGI发展 Alpha Zero时刻 • Alpha Go采用监督学习， 使用人类棋谱训练 • Alpha Zero采用强化学习， 自己跟自己对弈 ChatGPT时刻 • OpenAI ChatGPT大模型， 通过预训练方式，实现涌 现，理解人类语言和知识

2022? A: Qatar Q: Who won the first women ’s fifa world cup? A: United States Q: When did miami vice go off the air? A: 1989 Q: Who wrote the song shout to the lord? A: Darlene Zschech Q: Who was thrown