How Tencent’s LLM Powers Real‑World AI Solutions with RAG and Agents

Introduction

In this article we explore Tencent's large language model (LLM) applications across multiple business scenarios, highlighting how cutting‑edge techniques improve model intelligence and user experience. We first introduce the broad range of use cases—content generation, intelligent customer service, role‑playing, and more—then analyze Retrieval‑Augmented Generation (RAG), GraphRAG for role‑playing, and Agent technology for goal‑driven tasks.

Core Application Scenarios

Content Generation: e.g., ad copy creation, comment assistance.

Content Understanding: e.g., text moderation, fraud detection.

Intelligent Customer Service: knowledge Q&A, user guidance.

Development Copilot: automated code review, test‑case generation.

Role‑Playing: intelligent NPC interaction in game environments.

LLM Application Technologies

(1) SFT (Supervised Fine‑Tuning) – fine‑tunes a base model with domain‑specific data, embedding business knowledge to enable targeted task responses.

(2) RAG (Retrieval‑Augmented Generation) – integrates external knowledge bases and retrieval mechanisms during generation, enhancing explainability and reducing hallucinations; commonly used in smart customer service and document assistants.

(3) Agent – equips the model with external tools to perform multi‑step reasoning, planning, and execution for complex tasks.