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
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