Prompt Engineering for Generative AI - Helion

ebook

Autor: James Phoenix, Mike Taylor
ISBN: 9781098153397
stron: 422, Format: ebook
Data wydania: 2024-05-16
Księgarnia: Helion

Cena książki: 254,15 zł (poprzednio: 299,00 zł)
Oszczędzasz: 15% (-44,85 zł)

Osoby, które kupiły tę książkę, wybierały także »

Large language models (LLMs) and diffusion models such as ChatGPT and Stable Diffusion have unprecedented potential. Because they have been trained on all the public text and images on the internet, they can make useful contributions to a wide variety of tasks. And with the barrier to entry greatly reduced today, practically any developer can harness LLMs and diffusion models to tackle problems previously unsuitable for automation.

With this book, you'll gain a solid foundation in generative AI, including how to apply these models in practice. When first integrating LLMs and diffusion models into their workflows, most developers struggle to coax reliable enough results from them to use in automated systems. Authors James Phoenix and Mike Taylor show you how a set of principles called prompt engineering can enable you to work effectively with AI.

Learn how to empower AI to work for you. This book explains:

The structure of the interaction chain of your program's AI model and the fine-grained steps in between
How AI model requests arise from transforming the application problem into a document completion problem in the model training domain
The influence of LLM and diffusion model architecture—and how to best interact with it
How these principles apply in practice in the domains of natural language processing, text and image generation, and code

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Spis treści

Prompt Engineering for Generative AI eBook -- spis treści

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