retrieval‑augmented generation — 5 Technical Articles

Mar 11, 2026 · Artificial Intelligence

How Gemini Embedding 2 Gives AI True Five‑Senses Perception

Google's Gemini Embedding 2 unifies text, image, video, audio, and document processing into a single multimodal embedding space, offering massive token capacity, multilingual support, and interleaved input, which dramatically improves retrieval speed, recall, and the quality of AI‑generated content across diverse applications.

Gemini Embedding 2Unified Embedding Spaceembedding model

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How Gemini Embedding 2 Gives AI True Five‑Senses Perception

JD Cloud Developers

Jul 23, 2025 · Artificial Intelligence

How Causal Inference Meets Large Language Models to Revolutionize E‑commerce Pricing

At QCon 2025, the author presented a novel approach that integrates causal inference with large language models using Retrieval‑Augmented Generation, process rewards, and tree‑search to generate explainable, accurate e‑commerce pricing recommendations, dramatically improving accuracy from 44% to 74% while cutting inference time to seconds.

causal inferencee‑commerce pricingreinforcement learning

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How Causal Inference Meets Large Language Models to Revolutionize E‑commerce Pricing

Alibaba Cloud Developer

Feb 10, 2025 · Artificial Intelligence

Understanding the AI Wave: A Deep Dive into Large Models and Their Impact

This article offers a comprehensive overview of large models, covering their historical evolution, technical foundations, the current "hundred‑model" competition, practical use cases across industries, and future challenges such as safety, controllability, and efficient deployment.

Scaling Lawlarge modelsretrieval‑augmented generation

0 likes · 33 min read

Understanding the AI Wave: A Deep Dive into Large Models and Their Impact

DataFunSummit

Apr 13, 2024 · Artificial Intelligence

Understanding and Mitigating Hallucinations in Large Language Model Industry Q&A with Knowledge Graphs

This article examines why large language models often produce hallucinations in industry question‑answering, defines the phenomenon, explores its data and training origins, proposes evaluation metrics, and presents practical strategies—including high‑quality fine‑tuning data, honest refusal mechanisms, advanced decoding methods, and external knowledge‑graph augmentation—to reduce hallucinations and improve reliability.

AI evaluationKnowledge Graphhallucination

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Understanding and Mitigating Hallucinations in Large Language Model Industry Q&A with Knowledge Graphs

DataFunTalk

Feb 10, 2024 · Artificial Intelligence

Mitigating Hallucinations in Large Language Model Applications with Knowledge Graphs

This article examines the challenges of using large language models for industry Q&A, defines hallucination phenomena, evaluates their causes and impact, and proposes a set of strategies—including high‑quality fine‑tuning data, honest alignment, advanced decoding, and external knowledge‑graph augmentation—to reduce hallucinations and improve answer reliability.

Knowledge Graphhallucinationlarge language models

0 likes · 21 min read

Mitigating Hallucinations in Large Language Model Applications with Knowledge Graphs