Agentic workflow of LLM

Agentic Design Patterns

https://www.deeplearning.ai/the-batch/how-agents-can-improve-llm-performance/?ref=dl-staging-website.ghost.io

• Reflection: The LLM examines its own work to come up with ways to improve it. 
• Tool Use: The LLM is given tools such as web search, code execution, or any other function to help it gather information, take action, or process data.
• Planning: The LLM comes up with, and executes, a multistep plan to achieve a goal (for example, writing an outline for an essay, then doing online research, then writing a draft, and so on).
• Multi-agent collaboration: More than one AI agent work together, splitting up tasks and discussing and debating ideas, to come up with better solutions than a single agent would.

 

Read "Agentic Design Patterns Part 2: Reflection"

Read "Agentic Design Patterns Part 3, Tool Use"

Read "Agentic Design Patterns Part 4: Planning"

Read "Agentic Design Patterns Part 5: Multi-Agent Collaboration"

 

ChatDev

https://mp.weixin.qq.com/s/gtjb1-jCchS2WqHjOIMOkg

由 OpenBMB 、面壁智能及清华大学NLP实验室于去年7月共同开源。开源不到一周时间就收获 2.7k+ star，连续3天位居 GitHub Trending  榜首。

近日，斯坦福大学吴恩达教授在一次演讲中表示：“基于 GPT-3.5 构建的智能体工作流在应用中表现比 GPT-4 要好。AI 智能体工作流将在今年推动人工智能取得巨大进步，甚至可能超过下一代基础模型，这是一个值得所有人关注的趋势。”

其中，吴恩达教授点赞了来自 OpenBMB 开源社区的 ChatDev 项目，为大家详细阐述了“多智能体协作”的趣味和魅力所在——开一家游戏公司只需要一个 ChatDev 就够了：当给到不同的 prompt 时，ChatDev 可以分饰多个AI智能体，分别扮演软件公司的 CEO、产品经理、设计师、测试人员......