From b978b9645e153e3d6fb7839dd5f905e623bc9e75 Mon Sep 17 00:00:00 2001 From: Preet Shah Date: Fri, 8 Nov 2024 16:47:53 +0000 Subject: [PATCH] fix: corrected links that have moved --- 04-prompt-engineering-fundamentals/README.md | 2 +- 17-ai-agents/translations/tw/README.md | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/04-prompt-engineering-fundamentals/README.md b/04-prompt-engineering-fundamentals/README.md index aaa5e16a9..feab47be6 100644 --- a/04-prompt-engineering-fundamentals/README.md +++ b/04-prompt-engineering-fundamentals/README.md @@ -281,7 +281,7 @@ Another technique for using primary content is to provide _cues_ rather than exa A prompt template is a _pre-defined recipe for a prompt_ that can be stored and reused as needed, to drive more consistent user experiences at scale. In its simplest form, it is simply a collection of prompt examples like [this one from OpenAI](https://platform.openai.com/examples?WT.mc_id=academic-105485-koreyst) that provides both the interactive prompt components (user and system messages) and the API-driven request format - to support reuse. -In it's more complex form like [this example from LangChain](https://python.langchain.com/docs/modules/model_io/prompts/prompt_templates/?WT.mc_id=academic-105485-koreyst) it contains _placeholders_ that can be replaced with data from a variety of sources (user input, system context, external data sources etc.) to generate a prompt dynamically. This allows us to create a library of reusable prompts that can be used to drive consistent user experiences **programmatically** at scale. +In it's more complex form like [this example from LangChain](https://python.langchain.com/docs/how_to/#prompt-templates/?WT.mc_id=academic-105485-koreyst) it contains _placeholders_ that can be replaced with data from a variety of sources (user input, system context, external data sources etc.) to generate a prompt dynamically. This allows us to create a library of reusable prompts that can be used to drive consistent user experiences **programmatically** at scale. Finally, the real value of templates lies in the ability to create and publish _prompt libraries_ for vertical application domains - where the prompt template is now _optimized_ to reflect application-specific context or examples that make the responses more relevant and accurate for the targeted user audience. The [Prompts For Edu](https://github.com/microsoft/prompts-for-edu?WT.mc_id=academic-105485-koreyst) repository is a great example of this approach, curating a library of prompts for the education domain with emphasis on key objectives like lesson planning, curriculum design, student tutoring etc. diff --git a/17-ai-agents/translations/tw/README.md b/17-ai-agents/translations/tw/README.md index fbafe93cb..2fb2e1921 100644 --- a/17-ai-agents/translations/tw/README.md +++ b/17-ai-agents/translations/tw/README.md @@ -38,7 +38,7 @@ AI Agents 允許大型語言模型（LLMs）透過提供**狀態**和**工具** ## LangChain Agents -[LangChain Agents](https://python.langchain.com/docs/modules/agents/?WT.mc_id=academic-105485-koreyst) 是我們上述定義的實現。 +[LangChain Agents](https://python.langchain.com/docs/how_to/#agents?WT.mc_id=academic-105485-koreyst) 是我們上述定義的實現。 要管理**狀態**，它使用一個名為 `AgentExecutor` 的內建函式。這個函式接受定義的 `agent` 和可用的 `tools`。