- 生成式AI的提示工程(影印版)(英文版)
- - 作者：(美)詹姆斯·菲尼克斯//迈克·泰勒|责编:张烨
  - 出版社：东南大学
  - ISBN：9787576617634
  - 出版日期：2025/02/01
  - 页数：401
- 售价：75.2

内容大纲
ChatGPT和DALL-E这样的大语言模型（LLM）和扩散模型拥有前所未有的潜力。通过使用互联网上的公共文本和图像进行训练，这些模型能够为各种任务提供帮助。而且，随着准入门槛的显著降低，几乎任何开发人员都可以利用AI模型来解决以前不适合自动化的问题。
借助本书，你将在生成式人工智能方面打下坚实的基础，学会如何在实践中应用这些模型。在将大语言模型和扩散模型集成到工作流中时，大多数开发人员很难获得可用于自动化系统的可靠结果。作者James Phoenix和Mike Taylor展示了如何通过提示工程原则在生产过程中有效使用AI。
作者介绍
目录
Preface
1. The Five Principles of Prompting
  Overview of the Five Principles of Prompting
  1. Give Direction
  2. Specify Format
  3. Provide Examples
  4. Evaluate Quality
  5. Divide Labor
  Summary
2. Introduction to Large Language Models for Text Generation
  What Are Text Generation Models?
    Vector Representations: The Numerical Essence of Language
    Transformer Architecture: Orchestrating Contextual Relationships
    Probabilistic Text Generation: The Decision Mechanism
  Historical Underpinnings: The Rise of Transformer Architectures
  OpenAI's Generative Pretrained Transformers
    GPT-3.5-turbo and ChatGPT
  GPT-4
  Google's Gemini
  Meta's Llama and Open Source
  Leveraging Quantization and LoRA
  Mistral
  Anthropic: Claude
  GPT-4V(ision)
  Model Comparison
   Summary
3. Standard Practices for Text Generation with ChatGPT
  Generating Lists
  Hierarchical List Generation
  When to Avoid Using Regular Expressions
  Generating JSON
    YAML
  Filtering YAML Payloads
  Handling Invalid Payloads in YAML
  Diverse Format Generation with ChatGPT
    Mock CSV Data
  Explain It like I'm Five
  Universal Translation Through LLMs
  Ask for Context
  Text Style Unbundling
  Identifying the Desired Textual Features
  Generating New Content with the Extracted Features
  Extracting Specific Textual Features with LLMs
  Summarization
  Summarizing Given Context Window Limitations
  Chunking Text
    Benefits of Chunking Text
    Scenarios for Chunking Text
    Poor Chunking Example
  Chunking Strategies

  Sentence Detection Using SpaCy
  Building a Simple Chunking Algorithm in Python
  Sliding Window Chunking
  Text Chunking Packages
  Text Chunking with Tiktoken
  Encodings
    Understanding the Tokenization of Strings
  Estimating Token Usage for Chat API Calls
  Sentiment Analysis
    Techniques for Improving Sentiment Analysis
    Limitations and Challenges in Sentiment Analysis
  Least to Most
    Planning the Architecture
    Coding Individual Functions
    Adding Tests
    Benefits of the Least to Most Technique
    Challenges with the Least to Most Technique
  Role Prompting
  Benefits of Role Prompting
  Challenges of Role Prompting
  When to Use Role Prompting
  GPT Prompting Tactics
    Avoiding Hallucinations with Reference
    Give GPTs "Thinking Time"
    The Inner Monologue Tactic
    Self-Eval LLM Responses
  Classification with LLMs
  Building a Classification Model
  Majority Vote for Classification
  Criteria Evaluation
  Meta Prompting
  Summary
4. Advanced Techniques for Text Generation with LangChain
  Introduction to LangChain
    Environment Setup
  Chat Models
  Streaming Chat Models
   Creating Multiple LLM Generations
  LangChain Prompt Templates
  LangChain Expression Language (LCEL)
  Using PromptTemplate with Chat Models
  Output Parsers
  LangChain Evals
  OpenAI Function Calling
  Parallel Function Calling
  Function Calling in LangChain
  Extracting Data with LangChain
  Query Planning
  Creating Few-Shot Prompt Templates
    Fixed-Length Few-Shot Examples

    Formatting the Examples
    Selecting Few-Shot Examples by Length
  Limitations with Few-Shot Examples
  Saving and Loading LLM Prompts
  Data Connection
  Document Loaders
  Text Splitters
  Text Splitting by Length and Token Size
  Text Splitting with Recursive Character Splitting
  Task Decomposition
  Prompt Chaining
    Sequential Chain
    itemgetter and Dictionary Key Extraction
    Structuring LCEL Chains
    Document Chains
    Stuff
    Refine
    Map Reduce
    Map Re-rank
  Summary
5. Vector Databases with FAISS and Pinecone
  Retrieval Augmented Generation (RAG)
  Introducing Embeddings
  Document Loading
  Memory Retrieval with FAISS
  RAG with LangChain
  Hosted Vector Databases with Pinecone
  Self-Querying
  Alternative Retrieval Mechanisms
  Summary
6. Autonomous Agents with Memory and Tools
  Chain-of-Thought
  Agents
    Reason and Act (ReAct)
    Reason and Act Implementation
    Using Tools
  Using LLMs as an API (OpenAI Functions)
  Comparing OpenAI Functions and ReAct
    Use Cases for OpenAI Functions
    ReAct
    Use Cases for ReAct
  Agent Toolkits
  Customizing Standard Agents
  Custom Agents in LCEL
  Understanding and Using Memory
    Long-Term Memory
    Short-Term Memory
    Short-Term Memory in QA Conversation Agents
  Memory in LangChain
    Preserving the State

    Querying the State
    ConversationBufferMemory
  Other Popular Memory Types in LangChain
    ConversationBufferWindowMemory
    ConversationSummaryMemory
    ConversationSummaryBufferMemory
    ConversationTokenBufferMemory
  OpenAI Functions Agent with Memory
  Advanced Agent Frameworks
    Plan-and-Execute Agents
    Tree of Thoughts
  Callbacks
    Global (Constructor) Callbacks
    Request-Specific Callbacks
    The Verbose Argument
    When to Use Which?
    Token Counting with LangChain
  Summary
7. Introduction to Diffusion Models for Image Generation
  OpenAI DALL-E
  Midjourney
  Stable Diffusion
  Google Gemini
  Text to Video
  Model Comparison
  Summary
8. Standard Practices for image Generation with Midjourney
  Format Modifiers
  Art Style Modifiers
  Reverse Engineering Prompts
  Quality Boosters
  Negative Prompts
  Weighted Terms
  Prompting with an Image
  Inpainting
  Outpainting
  Consistent Characters
  Prompt Rewriting
  Meme Unbundling
  Meme Mapping
  Prompt Analysis
  Summary
9. Advanced Techniques for Image Generation with Stable Diffusion
  Running Stable Diffusion
  AUTOMATIC1111 Web User Interface
  Img2Img
  Upscaling Images
  Interrogate CLIP
  SD Inpainting and Outpainting
  ControlNet

  Segment Anything Model (SAM)
  DreamBooth Fine-Tuning
  Stable Diffusion XL Refiner
  Summary
10. Building AI-Powered Applications
  AI Blog Writing
  Topic Research
  Expert Interview
  Generate Outline
  Text Generation
  Writing Style
  Title Optimization
  AI Blog Images
  User Interface
  Summary
Index

内容大纲

作者介绍

目录

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