🧠 Practical LLM Engineering

A hands-on, engineering-focused learning resource for Large Language Models and Generative AI systems.

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🎯 What This Repository Is

Most LLM tutorials focus on prompting or high-level concepts. This repository goes deeper — covering how modern LLM systems actually work under the hood, from tokenization to production inference pipelines.

Every topic ships as a self-documented Jupyter notebook with:

• Clear conceptual explanations
• Architecture diagrams
• Runnable implementation code
• Practical experiments
• Engineering insights you won't find in papers

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🗺️ Learning Path

01 Fundamentals          → Tokens, embeddings, attention, generation
02 Prompting             → Zero-shot, few-shot, CoT, structured prompts
03 Embeddings & Retrieval → Sentence embeddings, vector DBs, hybrid search
04 RAG Systems           → Basic → Advanced → Agentic RAG
05 LLM Agents            → Tool use, function calling, memory, multi-step
06 Evaluation            → Perplexity, BLEU/ROUGE, LLM-as-judge
07 Fine-tuning           → LoRA, QLoRA, instruction tuning
08 Alignment             → RLHF, DPO, reward models
09 Inference Engineering → KV cache, batching, quantization, streaming
10 Advanced Inference    → Speculative decoding, EAGLE, Medusa
11 Systems Engineering   → Production pipelines, GPU memory, edge deployment

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📂 Repository Structure

practical-llm-engineering/
├── README.md
├── ROADMAP.md
├── setup/
│   ├── requirements.txt
│   └── environment_setup.ipynb
├── 01_fundamentals/
├── 02_prompting/
├── 03_embeddings_and_retrieval/
├── 04_rag_systems/
├── 05_llm_agents/
├── 06_llm_evaluation/
├── 07_finetuning/
├── 08_alignment/
├── 09_llm_inference_engineering/
├── 10_advanced_inference/
├── 11_llm_systems_engineering/
├── projects/
│   ├── rag_chatbot/
│   ├── research_paper_assistant/
│   └── code_generation_agent/
├── utils/
└── figures/

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🚀 Quick Start

# 1. Clone the repository
git clone https://github.com/your-username/practical-llm-engineering.git
cd practical-llm-engineering

# 2. Create virtual environment
python -m venv .venv
source .venv/bin/activate  # Windows: .venv\Scripts\activate

# 3. Install dependencies
pip install -r setup/requirements.txt

# 4. Launch Jupyter
jupyter lab

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📚 Module Overview

#	Module	Key Topics	Notebooks
01	Fundamentals	Tokenization, attention, sampling	5
02	Prompting	Zero-shot, CoT, structured	5
03	Embeddings & Retrieval	Vector DBs, hybrid search	5
04	RAG Systems	Pipeline, reranking, evaluation	5
05	LLM Agents	Tools, memory, multi-step	4
06	Evaluation	Perplexity, BLEU, LLM-judge	4
07	Fine-tuning	LoRA, QLoRA, datasets	4
08	Alignment	RLHF, DPO, reward models	5
09	Inference Engineering	KV cache, batching, quantization	5
10	Advanced Inference	Speculative, EAGLE, Medusa	5
11	Systems Engineering	Production, GPU, edge	8

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🧩 Notebook Structure

Every notebook follows a consistent format:

1. Overview & Learning Objectives
2. Background Concepts
3. Architecture Diagram
4. Step-by-Step Implementation
5. Experiments & Benchmarks
6. Engineering Notes & Gotchas
7. Exercises
8. References

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🤝 Contributing

Contributions are welcome! See CONTRIBUTING.md for guidelines.

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📄 License

MIT License — see LICENSE for details.