---
title: "Unique Friend | InfiniFlow’s Zhang Yingfeng: RAG Will Grow Fast in 2024"
author: "Unique Research"
sourcePublication: "Unique Research (FFCAP English library)"
originalPublishedAt: "2024-01-12T18:00:00+08:00"
canonical: "https://ffcap.cn/en/research/20240112a07x8r00"
source: "https://ffcap.cn/en/research/20240112a07x8r00"
language: "en"
cnSourceUrl: "https://view.inews.qq.com/a/20240112A07X8R00"
---

# Unique Friend | InfiniFlow’s Zhang Yingfeng: RAG Will Grow Fast in 2024

_Unique Research · Insight / Unique Friend Q&A · January 12, 2024 · Guest: Zhang Yingfeng, Founder, InfiniFlow (英飞流)_

Inventory type remains **insight** (Unique Friend series — not partner welcome).

## Guest

**Zhang Yingfeng** — Serial founder: 7 years search engines; 5 years DB kernels; 10 years cloud/big-data infra; 10 years AI algorithms (ads, CV, NLP). Led three large enterprise digital transformations; supported C-end e-commerce with millions DAU and ~200M daily dynamic search requests.

**InfiniFlow** — Shanghai, Aug 2023; AI infra for universal enterprise AI benefit. Flagship: AI-native database for RAG with LLMs; full industry RAG solutions end-to-end.

## Q&A

**1\. Product and core scenarios?**  
(1) **Infinity** AI-native DB for RAG — fixes vector-DB gaps serving RAG and future RAG evolution. (2) Full RAG solutions — DB alone is far from business; LLM landing is early; solutions accelerate digitalization.

**2\. R&D / GTM; biggest 2023 challenge and win?**  
DB open-sourced before end-2023 with community attention; first release ~Mar–Apr 2024 with seed trials. Parallel RAG solutions in multi-industry POC. Challenge: LLM hype vs enterprise-satisfying quality → hard paid conversion. Harvest: accelerating domestic RAG trials; customer feedback reshaped Infinity priorities toward present RAG pain points while keeping long view.

**3\. GenAI opportunities vs prior waves?**  
Digitization is no longer lip service. Prior AI stayed in limited marketing scenes; post-traffic-dividend needs efficiency/office apps — LLMs make general-purpose enterprise solutions possible (vs single-problem AI).

**4\. China landing challenges?**  
Domestic LLMs lag ChatGPT → scene limits; weaker pay willingness and expectation–reality gap; compute bottlenecks even with RAG — hope domestic GPUs/ecosystems catch up.

**5\. Competing with giants?**  
In infra, giants rarely win by dimensionality reduction — needs long accumulation; startups iterate faster with better usability. Giants often KPI wheel-reinvention, weak on features/usability/cost.

**6\. Are LLMs still too weak?**  
Far from ideal — severely limits apps. Benchmarks claiming near/over ChatGPT often collapse on real RAG scenes.

**7\. 2024 tech to watch?**  
Whether or not “Year of RAG,” fast RAG growth expected — plus RAG-boosting models (doc understanding/ranking). Multimodal + cheap open multimodal/MoE; inference accel via quantization/sparsity and new backbones. Hopes domestic/open LLMs break on real abilities (summarization, controllable hallucination) not just leaderboard wins.

_1 CN image; attach on ingest. Signup CTA in CN omitted._

— END —

---

Editorial English edition published on UniqueCapital: https://ffcap.cn/en/research/20240112a07x8r00
On-site reading page: https://ffcap.cn/en/research/20240112a07x8r00