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所在平台: Udemy |
课程主页: https://www.udemy.com/course/prompt-engineering-and-generative-ai-fundamentals/
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**课程名称：** 提示工程与生成式AI - 基础 **课程概覽：** 本课程深入探讨提示工程和生成式AI的基础概念，涵盖了提示工程基础、检索增强生成（RAG）、大型语言模型（LLM）微调以及LLM的保护机制（Guardrails）。 **内容摘要：** * **提示工程基础：** * 定义提示工程、最佳实践及Gemini-Pro模型的提示示例。 * 介绍LLM响应流式传输、Gemini-Pro模型的具体指令、温度和Token数量参数。 * 讲解零样本提示（Zero-Shot Prompting）技术及其在Gemini模型上的应用。 * 演示少样本提示（Few-shot Prompting）和思维链提示（Chain-of-Thought Prompting）技术及在Gemini模型上的应用。 * 设置Google Colab笔记本以使用OpenAI的GPT模型，并展示思维之树（Tree-of-Thoughts）提示技术，包括使用Langchain解决4x4数独谜题的实现。 * **检索增强生成（RAG）：** * 定义RAG技术、其优点以及使用Langchain框架将RAG应用于CSV文件的示例。 * 通过Arxiv Loader、FAISS向量数据库和Conversational Retrieval Chain在Langchain框架下的RAG流水线进行详细示例。 * 讲解使用RAGAS框架评估LLM响应。 * 展示Langsmith在RAGAS框架之外，用于LLM响应评估的complementary作用。 * 介绍使用Gemini模型创建文本嵌入和执行文档搜索。 * **大型语言模型微调：** * 总结提示技术，通过Hugging Face LLM示例，解释提示LLM与微调LLM的区别。 * 定义LLM微调、LLM微调的类型，以及在微调LLM前提取数据进行EDA（包括数据清洗）。 * 详细讲解在特定任务的标记数据集上微调预训练LLM。 * **大型语言模型的保护机制（Guardrails）：** * 定义Guardrails，并提供OpenAI的Guardrails示例。 * 讨论开源Guardrails实现，重点关注GuardrailsAI在信息提取方面的应用。 * 解释GuardrailsAI在生成结构化数据方面的应用，以及GuardrailsAI与Chat Model的接口。 **每部分均包含相应的Google Colab笔记本。**
This course delves into the fundamental concepts related to Prompt Engineering and Generative AI. The course has subsections on Fundamentals of Prompt Engineering, Retrieval Augmented Generation, Fine-tuning a large language model (LLM) and Guardrails for LLM. Section on Prompt Engineering Fundaments:The first segment provides a definition of prompt engineering, best practices of prompt engineering and an example of a prompt given to the Gemini-Pro model with references for further reading.The second segment explains what streaming a response is from a large language model, examples of providing specific instructions to the Gemini-Pro model as well as temperature and token count parameters. The third segment explains what Zero-Shot Prompting technique is with examples using the Gemini Model. The fourth segment explains Few-shot and Chain-of-Thought Prompting techniques with examples using the Gemini Model. Subsequent segments in this section shall discuss setting up the Google Colab notebook to work with the GPT model from OpenAI and provide examples of Tree-of-Thoughts prompting technique, including the Tree-of-Thoughts implementation from Langchain to solve the 4x4 Sudoku Puzzle. Section on Retrieval Augmented Generation (RAG):In this section, the first segment provides a definition of Retrieval Augmented Generation Prompting technique, the merits of Retrieval Augmented Generation and applying Retrieval Augmented Generation to a CSV file, using the Langchain framework In the second segment on Retrieval Augmented Generation, a detailed example involving the Arxiv Loader, FAISS Vector Database and a Conversational Retrieval Chain is shown as part of the RAG pipeline using Langchain framework.In the third segment on Retrieval Augmented Generation, evaluation of response from a Large Language Model (LLM) using the RAGAS framework is explained. In the fourth segment on Retrieval Augmented Generation, the use of Langsmith is shown complementing the RAGAS framework for evaluation of LLM response. In the fifth segment, use of the Gemini Model to create text embeddings and performing document search is explained. Section on Large Language Model Fine-tuning :In this section, the first segment provides a summary of prompting techniques with examples involving LLMs from Hugging Face repository and explaining the differences between prompting an LLM and fine-tuning an LLM. The second segment provides a definition of fine-tuning an LLM, types of LLM fine-tuning and extracting the data to perform EDA (including data cleaning) prior to fine-tuning an LLM. Third segment explains fine-tuning a pre-trained large language model on a task specific labeled dataset in detail. Section on Guardrails for Large Language Models:In this section, the first segment provides a definition of Guardrails as well as examples of Guardrails from OpenAI. In the second segment on Guardrails, examples of open source Guardrail implementations are discussed with a specific focus on GuardrailsAI for extracting information from text.In the third section, use of GuardrailsAI for generating structured data and interfacing GuardrailsAI with a Chat Model have been explained. Each of these segments has a Google Colab notebook included.
