#document embedding
818 related articles
·3 min
Replacing spaCy's Sentencizer with yasbd: Accuracy Jumps from 55% to 98.9%
spaCy's default Sentencizer achieves only 55.4% accuracy on edge cases, while open-source library yasbd reaches 98.9%. Analysis of limitations and integration code examples.
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·3 min
Why You Shouldn't Ask LLMs for Confidence Scores: The Pitfalls of Self-Reported Reliability
Asking LLMs to self-report confidence scores is a common mistake. Learn why it fails and discover reliable alternatives like logprobs, self-consistency sampling, and RAG.
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·3 min
Why You Shouldn't Ask LLMs for Confidence Scores: The Pitfalls of LLM Self-Assessed Reliability
Asking LLMs for self-reported confidence scores is a common mistake. Learn why it fails, and discover reliable alternatives like logprobs, self-consistency sampling, and RAG for uncertainty estimation.
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·4 min
Getting Started with AI Agent Development: A Complete Guide from Concept to Practice
A comprehensive guide to AI Agent architecture and development, covering automated marketing, intelligent customer service, and investment analysis scenarios with single and multi-agent collaboration.
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·3 min
A Beginner's Guide to AI Agents: Two Learning Paths for Developers and Practitioners
How to learn AI Agents from scratch? This guide covers two clear paths: developers go from Python to LLMs to open-source framework source code; practitioners use Claude Code or similar tools to get results fast.
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·3 min
Three Essential Hard Skills Traditional Product Managers Need to Transition into AI PM
How can traditional product managers transition to AI PM? This article analyzes the essential differences and details three must-have skills: AI product cognition, advanced Prompt engineering, and large model technical logic.
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·2 min
The Invisible Prompt Trap: How a Professor Caught 32 Students Cheating with AI in One Move
A professor embedded invisible prompts in assignments, catching 32 of 35 students using AI to cheat. Learn how this prompt injection trap works and what it means for education.
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·3 min
Carta: Pandoc Rewritten in Rust — 45x Faster, 20x Smaller
Carta is a Rust reimplementation of Pandoc with a 9MB binary (1/20th of Pandoc) and up to 45x faster conversion. Supports Markdown, DOCX, LaTeX, and Pandoc JSON filters.
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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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·3 min
From RAG to Agent Tuning: A Complete Guide to Deploying LLM Agents
A deep dive into LLM Agent frameworks covering RAG, Agent core components (tools, memory, planning), and Agent Tuning workflows with cost considerations for production deployment.
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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
Deconstructing Agents: Task Decomposition, Context Compression, and the Case Against Heavy Frameworks
A deep engineering analysis of Agent internals: how LLMs decompose tasks via tool calling, why context compression and memory are essential, and why solo developers should avoid heavy frameworks.
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·2 min
Invisible Prompt Traps: How a Professor Caught 32 Students Cheating with AI Using a Single Trick
A professor embedded invisible prompts in assignments, catching 32 of 35 students using AI to cheat. Learn how this prompt injection trap works and what it means for education.
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·3 min
Carta: Pandoc Rewritten in Rust — 45x Faster, 20x Smaller
Carta is a Rust reimplementation of Pandoc that produces a 9MB binary (1/20th of Pandoc) and achieves up to 45x faster conversion speeds for Markdown, DOCX, LaTeX, and more.
Read more →
·4 min
AI Agent Development in Practice: A Complete Path from Zero to Production
A systematic guide to AI Agent development covering core modules, framework selection, tool calling, data preparation, and production deployment to help developers build production-ready Agent applications.
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·4 min
A Beginner's Guide to AI Agents: Core Principles and Learning Paths Explained
A beginner's guide to AI Agents: understand core principles, how Agents differ from LLMs, their execution mechanisms, and get tailored learning path recommendations.
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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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·5 min
Getting Started with Codex: Four Product Forms Explained & How to Choose Between Cursor, Claude Code, and Codex
In-depth analysis of OpenAI Codex's four forms (CLI, web, plugin, app), comparing Codex, Claude Code, and Cursor on price, stability, and use cases to help developers choose the right tool.
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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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