How Tencent Leverages RAG, GraphRAG, and Agents to Power Large Language Model Applications
This article explores Tencent's large language model deployments across various business scenarios, detailing core use cases such as content generation, intelligent customer service, and role‑playing, and explains the underlying technologies—Supervised Fine‑Tuning, Retrieval‑Augmented Generation, and Agent systems—that enable these applications.
Introduction
In this article we dive into Tencent's large language model applications, focusing on how cutting‑edge techniques improve model intelligence and user experience.
Main Content Overview
Tencent large language 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 large model technology is applied in many domains such as the WeChat ecosystem, social content, video news, office documents, and games, driving intelligent and efficient solutions.
Content Generation: e.g., ad copy, 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 games.
Large Model Application Technologies
Tencent mainly uses three technical approaches:
(1) Supervised Fine‑Tuning (SFT)
Fine‑tunes a base 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; used in intelligent customer service and document assistants.
(3) Agent Systems
Leverages external tools to allow the model to perform multi‑step reasoning, planning, and execution for complex tasks.
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