Prompt, RAG, or fine-tuning: what actually makes your LLM learn

In this article, Sergey Proshchaev, a Tech Lead in FinTech & E-commerce, compares three approaches to teaching an LLM domain knowledge: prompting with context, RAG, and QLoRA fine-tuning. Using one model (Qwen3-8B) and one GPU (RTX 4090), he tested all three on the same 30 questions. The results show that RAG is best for factual questions, QLoRA excels at format and terminology, but fails on updated data, and prompting is a good starting point but has limitations.

Capital Heavy Bet on Edge Physical AI: Qianhai Fund Invests Hundreds of Millions, Om AI Lianhui Accelerates Commercialization

Om AI Lianhui (Hangzhou Lianhui Technology) has completed a new round of financing of several hundred million yuan, led by Qianhai Mother Fund, with participation from Hangzhou government industry funds and other investors. The company also open-sourced its VLX-Seek 1.5 edge-native fine-grained multimodal model, claiming performance surpassing NVIDIA's core model at the same parameter level.

NVIDIA