LLM Engineer's Handbook: Master the art of engineering large language models from concept to production

ABSTRACT

Moving an LLM project from a messy Jupyter notebook into a robust production environment is the primary challenge for AI teams today. This handbook offers a comprehensive roadmap for designing, training, and deploying open-source models using strict LLMOps best practices. The authors guide readers through a unified project—building an 'LLM Twin'—which covers advanced data engineering, Direct Preference Optimization (DPO), and low-latency inference optimization. It is a vital resource for implementing CI/CD pipelines in the generative AI era.