A collection of Jupyter notebooks accompanying the "Building LLMs for Production" book by Towards AI, providing practical tutorials on RAG, LLMs, prompt engineering, fine-tuning, and more.
One-Minute Overview#
This is a repository of Jupyter notebooks accompanying the "Building LLMs for Production" book by Towards AI, containing 10 chapters of practical tutorials covering the entire workflow from basic Transformers architecture to advanced RAG, fine-tuning, and agent development. It's ideal for AI engineers and data scientists who want to gain in-depth understanding and practical experience in developing LLM applications for production.
Core Value: Provides a complete path from theory to practice for LLM development, enabling readers to master core technologies for production-level LLM applications through executable notebooks.
Quick Start#
Installation Difficulty: Low - No installation required. All content runs directly in Google Colab, accessible with just a Google account.
# Access chapter notebooks directly via Google Colab links:
# Chapter 2: https://colab.research.google.com/github/towardsai/ragbook-notebooks/blob/main/notebooks/Chapter%2002%20-%20Transformers_Architectures.ipynb
# Chapter 5: https://colab.research.google.com/github/towardsai/ragbook-notebooks/blob/main/notebooks/Chapter%2005%20-%20Building_Applications_Powered_by_LLMs_with_LangChain.ipynb
# Chapter 8: https://colab.research.google.com/github/towardsai/ragbook-notebooks/blob/main/notebooks/Chapter%2008%20-%20Mastering_Advanced_RAG.ipynb
# Chapter 10: https://colab.research.google.com/github/towardsai/ragbook-notebooks/blob/main/notebooks/Chapter%2010%20-%20FineTuning_a%20LLM_QLoRA.ipynb
Is this suitable for me?
- ✅ Learning LLM application development: Master RAG, prompt engineering, fine-tuning and other technologies through practical notebooks
- ✅ Rapid prototyping: Run examples directly in Colab without local environment setup
- ✅ Technical solution reference: Provides concrete implementation ideas and code templates for projects
- ❌ Production deployment: Primarily a learning resource, not directly suitable for production deployment
- ❌ Enterprise customization: Requires further development based on specific business needs
Core Capabilities#
1. Complete LLM Application Development Workflow#
From basic Transformer architecture understanding to advanced RAG implementation, model fine-tuning, and agent development, providing end-to-end LLM application development guidance. Actual Value: Enables developers to systematically master the core technology stack of LLM application development, avoiding fragmented learning processes.
2. Practice-Oriented Tutorial Design#
Each chapter includes executable Jupyter notebooks with abundant code examples that can run directly in Google Colab. Actual Value: Readers don't need to configure complex local environments; they can quickly get started through "copy-run-modify" approach.
3. Diverse Technical Coverage#
Covers multiple cutting-edge fields including prompt engineering, LangChain, LlamaIndex, model fine-tuning, and evaluation methods. Actual Value: Helps developers comprehensively understand various tools and frameworks in the LLM ecosystem, providing references for technology selection.
4. Hierarchical Learning Path#
Progresses from fundamental concepts (Chapter 2) to advanced applications (Chapter 10), suitable for learners with different backgrounds. Actual Value: Both beginners and experienced developers can find learning content that suits their level.
Technology Stack and Integration#
Development Language: Python (Jupyter Notebook) Main Dependencies: Transformers, LangChain, LlamaIndex, OpenAI API, Cohere API Integration Method: Provides online execution environment through Google Colab, no local installation required