LLM-Engineers-Handbook: what it is, what problem it solves & why it's gaining traction

What it solves

This project provides a comprehensive, production-ready framework for building end-to-end LLM-based systems. It bridges the gap between simple demos and professional deployment by implementing best practices for data collection, model training, RAG (Retrieval-Augmented Generation), and cloud infrastructure management.

How it works

The system is built using Domain-Driven Design (DDD) principles and orchestrated via ZenML to manage ML pipelines. It integrates several specialized tools:

• Training & Evaluation: Uses AWS SageMaker for compute, Comet ML for experiment tracking, and Hugging Face for model registry.
• RAG & Data: Employs Qdrant as a vector database and MongoDB as a NoSQL data warehouse.
• Deployment: Leverages AWS for production-ready hosting, FastAPI for the inference REST API, and GitHub Actions for CI/CD pipelines.
• Monitoring: Uses Opik for prompt monitoring and evaluation.

Who it’s for

It is designed for LLM engineers and developers who want to move beyond basic prompts to create scalable, monitored, and maintainable AI applications in a production environment.

Highlights

• End-to-End Pipeline: Covers everything from data generation to production deployment.
• Production Infrastructure: Detailed integration with AWS SageMaker, ECR, and S3.
• MLOps Integration: Includes experiment tracking, prompt monitoring, and automated CI/CD.
• Modular Architecture: Organized into domain, application, infrastructure, and model layers for better maintainability.