فهرست مطالب

• What Experts Think About Building LLMs for Production
• Acknowledgement
• (Untitled)
• Preface
• (Untitled)
• Introduction
• Why Prompt Engineering, Fine-Tuning, and RAG?
• Coding Environment and Packages
• Learning Resources
• (Untitled)
• Chapter I: Introduction to LLMs
• What are Large Language Models
• Key LLM Terminologies
• From Language Models to Large Language Models
• History of NLP/LLMs
• Recap
• Chapter II: LLM Architectures and Landscape
• Understanding Transformers
• Transformer Model’s Design Choices
• The Generative Pre-trained Transformer (GPT) Architecture
• Introduction to Large Multimodal Models
• Proprietary vs. Open Models vs. Open-Source Language Models
• Applications and Use-Cases of LLMs
• Recap
• Chapter III: LLMs in Practice
• Understanding Hallucinations and Bias
• Evaluating LLM Performance
• Controlling LLM Outputs
• Pretraining and Fine-Tuning LLMs
• Recap
• Chapter IV: Introduction to Prompting
• Prompting and Prompt Engineering
• Bad Prompt Practices
• Tips for Effective Prompt Engineering
• Recap
• (Untitled)
• Chapter V: Introduction to LangChain & LlamaIndex
• LangChain Introduction
• LangChain Agents & Tools Overview
• Building LLM-Powered Applications with LangChain
• Building a News Articles Summarizer
• LlamaIndex Introduction
• LangChain vs. LlamaIndex vs. OpenAI Assistants
• Recap
• Chapter VI: Prompting with LangChain
• What are LangChain Prompt Templates
• Few-Shot Prompts and Example Selectors
• Managing Outputs with Output Parsers
• Improving Our News Articles Summarizer
• Creating Knowledge Graphs from Textual Data: Unveiling Hidden Connections
• Recap
• (Untitled)
• (Untitled)
• Chapter VII: Retrieval-Augmented Generation
• (Untitled)
• Retrieval-Augmented Generation
• LangChain’s Indexes and Retrievers
• Data Ingestion
• What are Text Splitters and Why They are Useful
• Tutorial: A Customer Support Q&A Chatbot
• Embeddings
• What are LangChain Chains
• Tutorial: A YouTube Video Summarizer Using Whisper and LangChain
• Tutorial: A Voice Assistant for Your Knowledge Base
• Preventing Undesirable Outputs With the Self-Critique Chain
• Recap
• Chapter VIII: Advanced RAG
• Prompting vs. Fine-Tuning vs. RAG
• Advanced RAG Techniques with LlamaIndex
• Production-Ready RAG Solutions with LlamaIndex
• RAG - Metrics & Evaluation
• LangChain’s LangSmith – Introduction
• Recap
• (Untitled)
• Chapter IX: Agents
• What are Agents: Large Models as Reasoning Engines
• An Overview of AutoGPT and BabyAGI
• The Agent Simulation Projects in LangChain
• Tutorial: Building Agents for Analysis Report Creation
• Tutorial: Query and Summarize a DB with LlamaIndex
• Building Agents with OpenAI Assistants
• Complement Your Agents Using Hugging Face’s APIs
• LangChain OpenGPT
• Tutorial: Multimodal Financial Document Analysis from PDFs
• Recap
• Chapter X: Fine-Tuning
• Techniques for Fine-Tuning LLMs
• Low-Rank Adaptation (LoRA)
• Practical Example: SFT with LoRA
• Using SFT for Financial Sentiment
• Fine-Tuning a Cohere LLM with Medical Data
• Reinforcement Learning from Human Feedback
• Tutorial: Improving LLMs with RLHF
• Recap
• Chapter XI: Deployment
• Challenges of LLM Deployment
• Model Quantization
• Model Pruning
• Deploying an LLM on a Cloud CPU
• Recap
• (Untitled)
• Conclusion