LLM Engineer's Handbook - Notes & Diagrams

Table of Contents

1. Resources & Community

2. Chapter Notes & Diagrams

2.1. Chapter 1: Understanding the LLM Twin Concept and Architecture

2.1.1. Core Concepts

  • Introduction to LLM Twin concept
  • System architecture principles
  • ML pipeline fundamentals

2.2. Chapter 2: Tooling and Installation

2.2.1. Key Components

  • Python ecosystem setup (Python 3.11.8)
  • MLOps/LLMOps tooling
  • MongoDB and vector databases
  • AWS configuration

2.3. Chapter 3: Data Engineering

2.3.1. Pipeline Overview

  • Data collection strategies
  • ETL process design
  • Warehouse integration

2.3.2. ETL Workflow

ch3-etl-workflow.png

2.4. Chapter 4: RAG Feature Pipeline

2.4.1. RAG Concepts

  • Retrieval-Augmented Generation basics
  • Advanced techniques
  • Feature pipeline design

2.5. Chapter 5: Supervised Fine-Tuning

2.5.1. Training Process

  • Instruction dataset creation
  • Fine-tuning techniques
  • Model evaluation

2.5.2. Training Flow

ch5-training-flow.png

2.6. Chapter 6: Fine-Tuning with Preference Alignment

2.6.1. Key Concepts

  • Preference datasets
  • Direct Preference Optimization (DPO)
  • Alignment techniques

2.7. Chapter 7: Evaluating LLMs

2.7.1. Evaluation Methods

  • Model metrics
  • RAG evaluation strategies
  • TwinLlama-3.1-8B analysis

2.8. Chapter 8: Inference Optimization

2.8.1. Optimization Strategies

  • Model parallelism
  • Quantization techniques
  • Performance tuning

2.9. Chapter 9: RAG Inference Pipeline

2.9.1. Pipeline Implementation

  • Advanced RAG techniques
  • Query optimization
  • Response generation

2.10. Chapter 10: Inference Pipeline Deployment

2.10.1. Deployment Strategy

  • Service architecture
  • Scaling patterns
  • Performance monitoring

2.11. Chapter 11: MLOps and LLMOps

2.11.1. DevOps Evolution

  • MLOps fundamentals
  • LLMOps specific practices
  • Cloud deployment

2.11.2. CI/CD Pipeline

ch11-cicd-pipeline.png

3. Usage Notes

  • Each diagram is an inline #+begin_src dot block; C-c C-c in Emacs re-renders the :file PNG (or run gmake diagrams)
  • The diagrams/ directory is created automatically on render
  • Graphviz (dot) required for diagram generation
  • Add notes and update diagrams as you study each chapter