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product#agent📝 BlogAnalyzed: Jan 16, 2026 01:16

Cursor's AI Command Center: A Deep Dive into Instruction Methods

Published:Jan 15, 2026 16:09
1 min read
Zenn Claude

Analysis

This article dives into the exciting world of Cursor, exploring its diverse methods for instructing AI, from Agents.md to Subagents! It's an insightful guide for developers eager to harness the power of AI tools, providing a clear roadmap for choosing the right approach for any task.
Reference

“The article aims to clarify the best methods for using various instruction features.”

PermalinkZenn Claude
product#llm📝 BlogAnalyzed: Jan 14, 2026 07:30

Unlocking AI's Potential: Questioning LLMs to Improve Prompts

Published:Jan 14, 2026 05:44
1 min read
Zenn LLM

Analysis

This article highlights a crucial aspect of prompt engineering: the importance of extracting implicit knowledge before formulating instructions. By framing interactions as an interview with the LLM, one can uncover hidden assumptions and refine the prompt for more effective results. This approach shifts the focus from directly instructing to collaboratively exploring the knowledge space, ultimately leading to higher quality outputs.
Reference

“This approach shifts the focus from directly instructing to collaboratively exploring the knowledge space, ultimately leading to higher quality outputs.”

PermalinkZenn LLM
product#agent📝 BlogAnalyzed: Jan 6, 2026 07:14

Demystifying Antigravity: A Beginner's Guide to Skills, Rules, and Workflows

Published:Jan 6, 2026 06:57
1 min read
Zenn Gemini

Analysis

This article targets beginners struggling to differentiate between various instruction mechanisms within the Antigravity (Gemini-based) environment. It aims to clarify the roles of Skills, Rules, Workflows, and GEMINI.md, providing a practical guide for effective utilization. The value lies in simplifying a potentially confusing aspect of AI agent development for newcomers.
Reference

“Antigravity を触り始めると、RulesやSkills、さらにWorkflowやGEMINI.mdといった“AI に指示する仕組み”がいくつも出てきて混乱しがちです 。”

PermalinkZenn Gemini
Technology#Artificial Intelligence, Image Generation, User Experience📝 BlogAnalyzed: Jan 4, 2026 05:50

Gemini Generates Images Unprompted, User Corrects Behavior

Published:Jan 3, 2026 15:48
1 min read
r/Bard

Analysis

The article describes a user's frustrating experience with Google's Gemini AI, which repeatedly generated images despite the user's explicit instructions not to. The user had to repeatedly correct the AI's behavior, eventually resolving the issue by adding a specific instruction to the 'Saved info' section. This highlights a potential issue with Gemini's image generation behavior and the importance of user control and customization options.
Reference

“The user's repeated attempts to stop image generation, and Gemini's eventual compliance after the 'Saved info' update, are key examples of the problem and solution.”

Permalinkr/Bard
Research#llm📝 BlogAnalyzed: Dec 24, 2025 22:25

Before Instructing AI to Execute: Crushing Accidents Caused by Human Ambiguity with Reviewer

Published:Dec 24, 2025 22:06
1 min read
Qiita LLM

Analysis

This article, part of the NTT Docomo Solutions Advent Calendar 2025, discusses the importance of clarifying human ambiguity before instructing AI to perform tasks. It highlights the potential for accidents and errors arising from vague or unclear instructions given to AI systems. The author, from NTT Docomo Solutions, emphasizes the need for a "Reviewer" system or process to identify and resolve ambiguities in instructions before they are fed into the AI. This proactive approach aims to improve the reliability and safety of AI-driven processes by ensuring that the AI receives clear and unambiguous commands. The article likely delves into specific examples and techniques for implementing such a review process.
Reference

“この記事はNTTドコモソリューションズ Advent Calendar 2025 25日目の記事です。”

PermalinkQiita LLM
Research#llm📝 BlogAnalyzed: Dec 24, 2025 18:44

Fine-tuning from Thought Process: A New Approach to Imbue LLMs with True Professional Personas

Published:Nov 28, 2025 09:11
1 min read
Zenn NLP

Analysis

This article discusses a novel approach to fine-tuning large language models (LLMs) to create more authentic professional personas. It argues that simply instructing an LLM to "act as an expert" results in superficial responses because the underlying thought processes are not truly emulated. The article suggests a method that goes beyond stylistic imitation and incorporates job-specific thinking processes into the persona. This could lead to more nuanced and valuable applications of LLMs in professional contexts, moving beyond simple role-playing.
Reference

“promptによる単なるスタイルの模倣を超えた、職務特有の思考プロセスを反映したペルソナ...”

PermalinkZenn NLP