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Data Thinking Notes

Sharing insights on data architecture, governance, and middle platforms, exploring AI in data, and linking data with business scenarios.

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Latest from Data Thinking Notes

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Data Thinking Notes

Sep 21, 2025 · Artificial Intelligence

From RAG to DeepSearch & DeepResearch: How AI Is Mastering Knowledge Retrieval

Amid the rapid rise of generative AI, this article examines the limitations of large language models and explains how Retrieval‑Augmented Generation (RAG), followed by the advanced paradigms DeepSearch and DeepResearch, progressively enhance knowledge handling through dynamic retrieval, multi‑agent reasoning, and autonomous research capabilities.

AI Knowledge ManagementDeepResearchDeepSearch

0 likes · 16 min read

From RAG to DeepSearch & DeepResearch: How AI Is Mastering Knowledge Retrieval

Data Thinking Notes

Sep 17, 2025 · Artificial Intelligence

Why ROI Should Drive AI Adoption in Finance, Not the Tech Race

This Tencent Research Institute and KPMG report examines large-model AI deployment in the financial sector, stressing that success hinges on ROI-driven strategies rather than a technology-for-technology's-sake race, and offers practical, forward-looking guidance on model building, scenario application, and digital transformation for financial institutions.

AIROIdigital transformation

0 likes · 6 min read

Why ROI Should Drive AI Adoption in Finance, Not the Tech Race

Data Thinking Notes

Sep 14, 2025 · Artificial Intelligence

How to Build a Robust Tool Integration Module for AI Agents

This article explains the architecture, core components, and step‑by‑step implementation of a tool usage module that enables AI agents to standardize, select, execute, and transform external tools, illustrated with a sales data analysis case and detailed code snippets.

AI AgentLLMTool Integration

0 likes · 9 min read

How to Build a Robust Tool Integration Module for AI Agents

Data Thinking Notes

Sep 10, 2025 · Artificial Intelligence

Why Do Language Models Hallucinate? Uncovering the Statistical Roots

OpenAI’s latest research reveals that language model hallucinations stem from training and evaluation incentives that favor confident guesses over acknowledging uncertainty, and proposes revised scoring methods that reward modesty, highlighting statistical mechanisms behind false answers and offering pathways to reduce hallucinations.

AI safetyUncertaintyevaluation

0 likes · 10 min read

Why Do Language Models Hallucinate? Uncovering the Statistical Roots

Data Thinking Notes

Sep 7, 2025 · Artificial Intelligence

Unlocking AI Agent Memory: How LLMs Use Retrieval and Planning to Stay Smart

This article explains the core architecture of AI agents powered by large language models, detailing how planning, short‑term and long‑term memory, and tool integration work together through vector databases, retrieval‑augmented generation, and summarization to enable stateful, intelligent interactions across multiple sessions.

AI AgentLLMMemory

0 likes · 10 min read

Unlocking AI Agent Memory: How LLMs Use Retrieval and Planning to Stay Smart

Data Thinking Notes

Sep 3, 2025 · Artificial Intelligence

What Makes a High‑Quality AI Dataset and How to Evaluate It?

This article defines what constitutes a high‑quality AI dataset, explains why such datasets are crucial—especially given the dominance of English resources and the scarcity in Chinese—and outlines the scientific evaluation framework covering completeness, accuracy, balance, timeliness, consistency, relevance, and other key dimensions.

AI datasetsdataset evaluationmachine learning

0 likes · 4 min read

What Makes a High‑Quality AI Dataset and How to Evaluate It?

Data Thinking Notes

Aug 31, 2025 · Artificial Intelligence

Embedding's Role in Retrieval‑Augmented Generation: Basics, Challenges & Future

This article explains how embedding technology converts unstructured data into vector representations, powers precise retrieval in Retrieval‑Augmented Generation (RAG), outlines the evolution of embedding models, discusses current challenges such as long‑text handling and domain adaptation, and highlights emerging solutions.

AIEmbeddingRAG

0 likes · 12 min read

Embedding's Role in Retrieval‑Augmented Generation: Basics, Challenges & Future

Data Thinking Notes

Aug 28, 2025 · Artificial Intelligence

How Large AI Models Transform Banking: Real-World Use Cases and Benefits

This Alibaba Cloud report examines how large AI models are moving beyond simple Q&A and office assistance to power complex financial tasks such as investment research, credit approval, and risk management, delivering cost savings, efficiency gains, better risk control, and enhanced user experiences for banks.

AICredit Approvalbanking

0 likes · 4 min read

How Large AI Models Transform Banking: Real-World Use Cases and Benefits

Data Thinking Notes

Aug 26, 2025 · Artificial Intelligence

From Prompt to Context: How AI Agents Evolve into Proactive Intelligence

This article explores the rapid growth of large language models and explains how AI agents transform passive, single‑turn responses into proactive, continuous intelligence by leveraging a core “Prompt→Context→Action” loop, detailing their architecture, key components, challenges, and future directions.

AI AgentContext ManagementLLM architecture

0 likes · 20 min read

From Prompt to Context: How AI Agents Evolve into Proactive Intelligence

Data Thinking Notes

Aug 21, 2025 · Artificial Intelligence

Why Intermediate Tokens Matter: Denny Zhou’s Deep Insights into LLM Reasoning

This article distills Denny Zhou’s Stanford CS25 lecture, explaining how large language models achieve reasoning through intermediate token generation, chain‑of‑thought prompting, self‑consistency, reinforcement‑learning fine‑tuning, and answer aggregation, while highlighting theoretical foundations and practical breakthroughs.

LLMReasoningchain of thought

0 likes · 18 min read

Why Intermediate Tokens Matter: Denny Zhou’s Deep Insights into LLM Reasoning