How Tencent’s Large Language Models Transform Business with RAG, GraphRAG, and Agents

Overview

In this article we explore Tencent’s large language model applications across various business scenarios, focusing on how cutting‑edge techniques such as Retrieval‑Augmented Generation (RAG), GraphRAG and autonomous agents enhance intelligence and user experience.

Main Topics

Tencent large model application scenarios

RAG technology principles and practice

GraphRAG in role‑playing scenarios

Agent technology principles and applications

Q&A session

Core Business Scenarios

Tencent’s models are deployed in the WeChat ecosystem, social content, video news, office documents, games and more, enabling content generation, understanding, intelligent customer service, development copilot, and NPC role‑playing.

Content generation: ad copy, comment assistance

Content understanding: 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 games

Key Model‑Driven Technologies

1. Supervised Fine‑Tuning (SFT)

Fine‑tunes a base large language model with domain‑specific data to embed business knowledge, enabling targeted task responses.

2. Retrieval‑Augmented Generation (RAG)

Combines external knowledge bases and retrieval mechanisms with generation, improving explainability and reducing hallucinations; applied in smart customer service and document assistants.

3. Autonomous Agents

Leverages external tools for multi‑step reasoning, planning and execution, suitable for complex tasks requiring several inference steps.