LLM Engineer's Handbook - Notes & Diagrams
Table of Contents
- Resources & Community
- Chapter Notes & Diagrams
- Chapter 1: Understanding the LLM Twin Concept and Architecture
- Chapter 2: Tooling and Installation
- Chapter 3: Data Engineering
- Chapter 4: RAG Feature Pipeline
- Chapter 5: Supervised Fine-Tuning
- Chapter 6: Fine-Tuning with Preference Alignment
- Chapter 7: Evaluating LLMs
- Chapter 8: Inference Optimization
- Chapter 9: RAG Inference Pipeline
- Chapter 10: Inference Pipeline Deployment
- Chapter 11: MLOps and LLMOps
- Usage Notes
Resources & Community
- Discord Server: https://packt.link/llmeng - Join the Packt Data & ML Community
- Book Website: https://www.packtpub.com/en-us/product/llm-engineers-handbook-9781836200079
- Color Images: https://static.packt-cdn.com/downloads/9781836200079_ColorImages.pdf
Chapter Notes & Diagrams
Chapter 1: Understanding the LLM Twin Concept and Architecture
Core Concepts
- Introduction to LLM Twin concept
- System architecture principles
- ML pipeline fundamentals
System Architecture
flowchart TD A[Raw Data Sources] -->|Collect| B[Feature Pipeline] B -->|Process| C[Vector Store] D[Training Data] -->|Fine-tune| E[Base Model] E -->|Deploy| F[Inference Pipeline] C -->|Retrieve| F F -->|Serve| G[API Endpoints] G -->|Response| H[Users]
Chapter 2: Tooling and Installation
Key Components
- Python ecosystem setup (Python 3.11.8)
- MLOps/LLMOps tooling
- MongoDB and vector databases
- AWS configuration
Chapter 3: Data Engineering
Pipeline Overview
- Data collection strategies
- ETL process design
- Warehouse integration
ETL Workflow
flowchart LR A[Web Sources] -->|Crawl| B[Raw Data] B -->|Extract| C[Text Content] C -->|Clean| D[Processed Text] D -->|Transform| E[Structured Data] E -->|Load| F[(MongoDB)] F -->|Index| G[(Vector Store)]
Chapter 4: RAG Feature Pipeline
RAG Concepts
- Retrieval-Augmented Generation basics
- Advanced techniques
- Feature pipeline design
Architecture Components
flowchart TD A[Document Chunks] -->|Embed| B[Embeddings] B -->|Store| C[(Vector DB)] D[User Query] -->|Embed| E[Query Embedding] E -->|Search| C C -->|Retrieve| F[Context] F -->|Augment| G[LLM] D -->|Input| G G -->|Generate| H[Response]
Chapter 5: Supervised Fine-Tuning
Training Process
- Instruction dataset creation
- Fine-tuning techniques
- Model evaluation
Training Flow
flowchart TD A[Base LLM] -->|Initialize| B[Training Process] C[Instruction Data] -->|Input| B B -->|Fine-tune| D[Trained Model] D -->|Evaluate| E[Model Metrics] E -->|Save| F[Model Registry] E -->|Iterate| B
Chapter 6: Fine-Tuning with Preference Alignment
Key Concepts
- Preference datasets
- Direct Preference Optimization (DPO)
- Alignment techniques
Chapter 7: Evaluating LLMs
Evaluation Methods
- Model metrics
- RAG evaluation strategies
- TwinLlama-3.1-8B analysis
Chapter 8: Inference Optimization
Optimization Strategies
- Model parallelism
- Quantization techniques
- Performance tuning
Chapter 9: RAG Inference Pipeline
Pipeline Implementation
- Advanced RAG techniques
- Query optimization
- Response generation
Advanced RAG Flow
flowchart TD A[Query] -->|Process| B[Query Understanding] B -->|Generate| C[Search Query] C -->|Search| D[Vector Store] D -->|Retrieve| E[Documents] E -->|Rerank| F[Ranked Results] F -->|Filter| G[Top K] G -->|Format| H[Prompt Template] H -->|Generate| I[Response] I -->|Post-process| J[Final Answer]
Chapter 10: Inference Pipeline Deployment
Deployment Strategy
- Service architecture
- Scaling patterns
- Performance monitoring
Service Architecture
flowchart TD A[Client] -->|Request| B[Load Balancer] B -->|Route| C[API Gateway] C -->|Validate| D[Auth Service] C -->|Process| E[Inference Service] E -->|Query| F[(Vector Store)] E -->|Generate| G[LLM Service] G -->|Log| H[(Monitoring DB)] E -->|Return| I[Response]
Chapter 11: MLOps and LLMOps
DevOps Evolution
- MLOps fundamentals
- LLMOps specific practices
- Cloud deployment
CI/CD Pipeline
flowchart LR A[Code] -->|Push| B[Git Repo] B -->|Trigger| C[CI Pipeline] C -->|Build| D[Docker Image] D -->|Push| E[Registry] E -->|Deploy| F[Model Service] F -->|Monitor| G[Metrics] G -->|Alert| H[Monitoring]
Usage Notes
- Each diagram can be generated using C-c C-c in Emacs
- The diagrams/ directory is created automatically
- Mermaid-mode required for diagram generation
- Add notes and update diagrams as you study each chapter