Prompt Engineering Guide
Prompt engineering is a relatively new discipline for developing and optimizing prompts to efficiently use language models (LMs) for a wide variety of applications and research topics. Prompt engineering skills help to better understand the capabilities and limitations of large language models (LLMs). Researchers use…
课时
- Guides 🔮
- Prompt Engineering Guide
- pages
- About
- About
- About
- 关于
- Agents
- LLM Applications & Guides
- プロンプトアプリケーション
- Prompting Applications
- 提示应用
- Prompt Engineering Course
- Prompt Engineering Courses
- Datasets
- データセット
- 数据集
- Prompt Engineering Guide
- Prompt Engineering Guide
- 프롬프트 엔지니어링 가이드
- 提示工程指南
- Introduction
- はじめに
- Introduction
- 提示工程简介
- Model Prompting Guides
- モデル
- Models
- 模型
- Prompt Engineering Notebooks
- Prompt Engineering Notebooks
- Prompt Engineering Notebooks
- Prompt Engineering笔记本
- Papers
- 論文
- Papers
- 论文
- Prompt Hub
- Prompt Hub
- Prompt Hub
- Prompt Hub
- Additional Readings
- 参考文献
- Additional Readings
- 阅读推荐
- LLM Research Findings
- LLM Research Findings
- LLM Research Findings
- LLM Research Findings
- Risks & Misuses
- リスクと誤用
- Risks & Misuses
- 风险和误用
- Services
- Services
- Prompting Techniques
- プロンプトエンジニアリング技術
- Prompting Techniques
- 提示技术
- Tools & Libraries
- ツール & ライブラリ
- Tools & Libraries
- 工具和库
- agents
- applications
- Generating Code
- コード生成
- Generating Code
- 代码生成
- Context Caching with Gemini 1.5 Flash
- Fine-Tuning with GPT-4o Models
- Function Calling with LLMs
- Function Calling with LLMs
- LLMs의 Function Calling
- 在LLM中调用函数
- Tackling Generated Datasets Diversity
- Generating Data
- データ生成
- 데이터 생성
- 生成数据
- Prompt Function
- Prompt Function
- 프롬프트 함수(Prompt Function)
- 提示函数
- Generating Synthetic Dataset for RAG
- Graduate Job Classification Case Study
- ケーススタディ: 新卒にふさわしい仕事の分類
- 학위가 필요한 직업을 분류한 사례 연구
- 毕业生职位分类案例研究
- guides
- introduction
- models
- ChatGPT Prompt Engineering
- ChatGPT プロンプトエンジニアリング
- ChatGPT 프롬프트 엔지니어링
- ChatGPT提示工程
- Claude 3
- Prompting Guide for Code Llama
- Prompting Guide for Code Llama
- Prompting Guide for Code Llama
- Prompting Guide for Code Llama
- LLM Collection
- モデル一覧
- 모델 선택
- Model Collection
- Scaling Instruction-Finetuned Language Models
- スケーリングされた指示によるファインチューニングされた言語モデル
- 지시에 따라 파인튜닝된(Instruction-Finetuned) 언어 모델 스케일링
- 扩展指令微调语言模型
- Gemini Advanced
- Gemini Advanced
- Gemini Advanced
- Gemini Advanced
- Gemini 1.5 Pro
- Gemini 1.5 Pro
- Gemini 1.5 Pro
- Gemini 1.5 Pro
- Getting Started with Gemini
- Gemini Prompting Guide
- Gemini Prompting Guide
- 开始使用Gemini
- Gemma
- GPT-4
- GPT-4
- GPT-4
- GPT-4
- Grok-1
- Kimi K2.5
- Llama 3
- Prompt Engineering Guide
- Prompt Engineering Guide
- Prompt Engineering Guide
- Prompt Engineering Guide
- Mistral 7B LLM
- Mistral 7B LLM
- Mistral 7B LLM
- Mistral 7B LLM
- Mistral Large
- Mixtral 8x22B
- Mixtral
- Mixtral
- Mixtral
- Mixtral
- OLMo
- OLMo
- OLMo
- OLMo
- Phi-2
- Phi-2
- Phi-2
- Phi-2
- Sora
- Sora
- Sora
- Sora
- prompts
- Adversarial Prompting in LLMs
- Adversarial Prompting in LLMs
- 大型语言模型中的对抗性提示
- LLMs for Classification
- LLMs for Classification
- 使用大型语言模型(LLMs)进行分类
- LLMs for Code Generation
- LLMs for Code Generation
- LLMs for Code Generation
- LLMs for Creativity
- LLMs for Creativity
- LLMs for Creativity
- LLM Evaluation
- LLM Evaluation
- LLM Evaluation
- Image Generation
- Image Generation
- Image Generation
- Information Extraction with LLMs
- Information Extraction with LLMs
- Information Extraction with LLMs
- Mathematical Understanding with LLMs
- Mathematical Understanding with LLMs
- Mathematical Understanding with LLMs
- Question Answering with LLMs
- Question Answering with LLMs
- Question Answering with LLMs
- Reasoning with LLMs
- Reasoning with LLMs
- Reasoning with LLMs
- Text Summarization with LLMs
- Text Summarization with LLMs
- Text Summarization with LLMs
- Truthfulness in LLMs
- Truthfulness in LLMs
- Truthfulness in LLMs
- adversarial-prompting
- classification
- coding
- creativity
- evaluation
- image-generation
- information-extraction
- mathematics
- question-answering
- reasoning
- text-summarization
- truthfulness
- research
- What is Groq?
- Groq 是什么?
- LM-Guided Chain-of-Thought
- 语言模型引导的思维链(CoT)
- Efficient Infinite Context Transformers
- 高效处理无限长文本的Transformer模型
- LLM Agents
- LLM Agents
- LLM Agents
- 大语言模型智能体简介
- LLM Reasoning
- 大语言模型的推理能力
- LLM In-Context Recall is Prompt Dependent
- 大型语言模型的上下文记忆受提示影响
- LLM Tokenization
- 大语言模型(LLM)的标记化处理
- Reducing Hallucination in Structured Outputs via RAG
- 利用 RAG 降低结构化输出中的虚假信息
- How Faithful are RAG Models?
- RAG 模型的忠实度如何?
- Retrieval Augmented Generation (RAG) for LLMs
- Retrieval Augmented Generation (RAG) for LLMs
- Retrieval Augmented Generation (RAG) for LLMs
- 大语言模型的检索增强生成 (RAG) 方法
- Best Practices and Lessons Learned on Synthetic Data for Language Models
- 语言模型合成数据的实用技巧与经验
- Reasoning with Intermediate Revision and Search for LLMs
- 大语言模型的中间修订与搜索推理
- Trustworthiness in LLMs
- Trustworthiness in LLMs
- Trustworthiness in LLMs
- 大语言模型的可信度探索
- risks
- techniques
- Active-Prompt
- アクティブプロンプト
- Active-Prompt
- Active-Prompt
- Automatic Prompt Engineer (APE)
- 自動プロンプトエンジニア(APE)
- Automatic Prompt Engineer (APE)
- 自动提示工程师(APE)
- Automatic Reasoning and Tool-use (ART)
- Automatic Reasoning and Tool-use (ART)
- Automatic Reasoning and Tool-use (ART)
- 自动推理并使用工具 (ART)
- Self-Consistency
- Self-Consistency(自己整合性)
- Self-Consistency
- 自我一致性
- Chain-of-Thought Prompting
- Chain-of-Thoughtプロンプティング
- Chain-of-Thought Prompting
- Chain-of-Thought Prompting
- Directional Stimulus Prompting
- 方向性刺激プロンプティング
- Directional Stimulus Prompting
- 方向性刺激提示
- Few-Shot Prompting
- Few-Shotプロンプティング
- Few-Shot Prompting
- 少样本提示
- GraphPrompts
- グラフプロンプト(GraphPrompts)
- GraphPrompts
- GraphPrompts
- Generated Knowledge Prompting
- 知識生成プロンプティング
- Generated Knowledge Prompting
- 生成知识提示
- Meta Prompting
- Multimodal CoT Prompting
- マルチモーダルCoTプロンプティング
- Multimodal CoT Prompting
- 多模态思维链提示方法
- PAL (Program-Aided Language Models)
- PAL(プログラム支援言語モデル)
- PAL (프로그램지원언어모델)
- PAL(程序辅助语言模型)
- Prompt Chaining
- Prompt Chaining
- Prompt Chaining
- 链式提示
- Retrieval Augmented Generation (RAG)
- 検索により強化された生成 (RAG)
- Retrieval Augmented Generation (RAG)
- 检索增强生成 (RAG)
- ReAct Prompting
- ReAct
- ReAct Prompting
- ReAct 框架
- Reflexion
- Reflexion
- Reflexion
- 自我反思(Reflexion)
- Tree of Thoughts (ToT)
- Tree of Thoughts (ToT)
- Tree of Thoughts (ToT)
- 思维树 (ToT)
- Zero-Shot Prompting
- Zero-Shotプロンプティング
- Zero-Shot Prompting
- 零样本提示
开始学习 → Advanced Prompting