#LLM hallucinations
327 related articles
Deep Dives·3 min
Getting Started with RAG: A Complete Guide from LLM Hallucinations to Retrieval-Augmented Generation
A deep dive into RAG (Retrieval-Augmented Generation) technology, covering LLM hallucinations, data staleness, and limited expertise, plus RAG workflows, core components, and LangChain learning paths.
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·5 min
A Beginner's Guide to Prompt Engineering: From One-Off Instructions to a Systematic Methodology
A beginner's guide to prompt engineering covering the four functions of prompts, the key differences from prompt engineering, a six-step systematic workflow, and critical technical and practical limitations.
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·4 min
A Beginner's Guide to AI Agents: Core Principles and Learning Paths Explained
Learn AI Agent core principles from scratch: understand how Agents differ from LLMs, their execution mechanisms, why rule design matters, and find the right learning path for your goals.
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·4 min
What Is the True Nature of Large Language Models? A Super Compressor Analogy to Help You Fully Understand
Large models aren't search engines — they're more like super compressors. This article explains how LLMs compress data to learn semantic patterns, and explores the phenomenon of intelligent emergence.
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·3 min
From Frontend to Agent Development: A Complete 6-Week Systematic Learning Path
A 6-week systematic learning path for frontend engineers transitioning to AI Agent development, covering core architecture, ReAct, multi-agent collaboration, RAG integration, and deployment.
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·3 min
Agent Skills Explained: Solving the MCP Context Bloat Problem
Deep dive into Anthropic's Agent Skills mechanism, explaining how Progressive Disclosure solves MCP context bloat and tool calling accuracy issues in AI agents.
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·5 min
Complete Breakdown of Core Competencies for AI Agent Engineers: From Demo to Production-Grade Systems
Deep breakdown of 4 core AI Agent engineer competencies: business decomposition, multi-Agent architecture, quantitative evaluation, and engineering delivery—bridging the gap from Demo to production.
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·4 min
What Is the True Nature of Large Language Models? A Super Compressor Analogy That Makes Everything Click
LLMs aren't search engines — they're more like super compressors. This article explains how large models compress corpora to learn semantic patterns, and explores the principles and limitations of emergent intelligence.
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·5 min
The Complete Guide to AI Agent Development: A Four-Stage Progressive Roadmap
A systematic guide to AI Agent development across four stages: LLM fundamentals, ReAct paradigm, memory & tools, and multi-agent collaboration for developers.
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·3 min
last30days: A Cross-Platform AI Research Tool and the Agent Skill with 50K+ Stars
last30days-skill is a GitHub AI Agent skill with 50K+ Stars, enabling cross-platform research across Reddit, X, YouTube, Hacker News, and Polymarket to generate grounded 30-day summary reports.
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·3 min
Simplified Technical English (STE): A Cross-Disciplinary Approach to Optimizing LLM Prompts
Explore how ASD-STE100 Simplified Technical English from aviation can be applied to LLM prompt engineering. STE's disambiguation principles—controlled vocabulary, short sentences, active voice—can improve LLM output accuracy and consistency.
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·2 min
How Users Gradually Learn to Trust Gemini: The Shift from Full Supervision to Selective Delegation
Exploring how users evolve trust in Gemini and generative AI—from verifying everything to selective delegation, analyzing trust patterns, key factors, and the trust drift trap.
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·4 min
The More Complex the AI Agent, the More It Hallucinates? The Hidden Cost of Architectural Complexity
Why do AI Agents hallucinate more as they grow more complex? This article analyzes the causes from error accumulation, context noise, and model completion nature, with 5 practical production strategies.
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·3 min
last30days: A Cross-Platform AI Research Tool and the Agent Skill with 50K+ Stars
last30days-skill is a 50K+ Star AI Agent skill on GitHub that performs cross-platform research across Reddit, X, YouTube, Hacker News, and Polymarket to generate grounded 30-day summary reports.
Read more →
·4 min
GenAI Job Interview Gone Wrong: Interviewer Googles Gemini for Questions on the Spot — What's Broken in AI Hiring?
A fresh grad interviewing for a GenAI Trainer role faced prime number coding and activation function questions while the interviewer used Gemini to generate questions live — exposing AI hiring chaos.
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·4 min
How Should Legal Education Transform in the AI Era? A Complete Guide from Curriculum Reform to Talent Development
Generative AI is profoundly disrupting the legal profession. This article explores AI's impact on law, law school curriculum reform, and the core competencies future lawyers need, including critical judgment, AI proficiency, and ethical literacy.
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·3 min
Better Tools Actually Made Copilot Code Review Worse? GitHub's Counterintuitive Optimization
GitHub found that giving Copilot more specialized tools actually degraded code review quality. By migrating to Unix-style composable tools and evidence-driven workflows, they cut costs and improved results.
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·3 min
Understand Anything: The AI Skill That Turns Code into Interactive Knowledge Graphs
Understand Anything is a high-star open-source GitHub skill that runs static analysis on any codebase and generates interactive knowledge graphs. It supports Claude Code, Cursor, Copilot and other agents, letting engineers ask questions in natural language with path references.
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·4 min
Claude Code from Scratch: A Complete Guide from Installation to Enterprise-Grade Development
A complete guide to Claude Code from beginner to enterprise practice: covering CLI installation, connecting domestic LLMs via CC Switch, basic commands, Git workflow integration, automated bug fixing, and engineering capabilities like MCP and SubAgents.
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·3 min
AI-Powered Bank-Enterprise Reconciliation in Practice: A Local Deployment Solution Where Data Never Leaves the Premises
Why is automated bank-enterprise reconciliation so hard to implement? This article explains how enterprises use AI Agents to handle fuzzy matching beyond hard rules, combining OCR with local all-in-one deployment for secure reconciliation where data never leaves the premises.
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