Prompt Engineering for Generative AI

Spis treści książki

• Preface
  • Software Requirements for This Book
  • Conventions Used in This Book
  • Using Code Examples
  • OReilly Online Learning
  • How to Contact Us
  • Acknowledgments
• 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
  • OpenAIs Generative Pretrained Transformers
    • GPT-3.5-turbo and ChatGPT
  • GPT-4
  • Googles Gemini
  • Metas 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 Im 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
      • When to chunk
      • When not to chunk
    • 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

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