Challenges and Responses in the Practice of Large Language Models

TL;DR

This paper surveys the challenges and responses in deploying large language models across five core dimensions—computing power infrastructure, software architecture, data resources, application scenarios, and brain science—and situates them within current industry trends and policy contexts. It synthesizes practical questions and approaches, covering cloud-edge-end architectures, private LLMs, fine-tuning versus RAG, data annotation pipelines, and knowledge-graph–grounded methods like GraphRAG, while addressing evaluation, security, and cross-domain applications. A central theme is the complementary relationship between big models and knowledge graphs, highlighting how their integration can improve grounding, interpretability, and scalability in real-world systems. By articulating concrete mechanisms, tradeoffs, and industrial implications, the work provides a framework to accelerate AI innovation and informed engineering practice across diverse domains.

Abstract

This paper carefully summarizes extensive and profound questions from all walks of life, focusing on the current high-profile AI field, covering multiple dimensions such as industry trends, academic research, technological innovation and business applications. This paper meticulously curates questions that are both thought-provoking and practically relevant, providing nuanced and insightful answers to each. To facilitate readers' understanding and reference, this paper specifically classifies and organizes these questions systematically and meticulously from the five core dimensions of computing power infrastructure, software architecture, data resources, application scenarios, and brain science. This work aims to provide readers with a comprehensive, in-depth and cutting-edge AI knowledge framework to help people from all walks of life grasp the pulse of AI development, stimulate innovative thinking, and promote industrial progress.