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Original rewrites on React, Node.js, TypeScript and AI — practical notes from the same engineering practice behind our operator software. Article bodies are in English.
Tagged: Prompt engineering
A Practical Playbook for Writing an Effective CLAUDE.md File
Learn 21 concrete, checkable rules for trimming a bloated CLAUDE.md file so Claude Code stays reliable, predictable, and easy to trust in long sessions.
3938 wordsRead articleAn Architecture Against Hallucination Compounding in Agent Graphs
Learn how to design a control-plane architecture with typed tools, citation ledgers, and schema gates that stops multi-agent LLM hallucinations from compounding.
2832 wordsRead articleBuilding Production-Ready LLM Prompts: A Seven-Layer Framework
Learn a structured, API-inspired framework of seven prompt layers—instructions, context, constraints, and more—for building reliable, production-grade LLM systems.
3297 wordsRead articleBuilding an AI Agent from Scratch: Patterns, ReAct, and LangGraph
Learn the core concepts behind AI agents—planning, tool use, reflection, and the ReAct pattern—and how LangChain and LangGraph fit into building one manually.
1163 wordsRead articleA Tiered Map of AI Engineering Concepts and When They Matter
Learn which AI engineering concepts determine whether a system works at all, which matter once you build for production, and which can wait.
2333 wordsRead articleTemperature, Top-K, and Top-P: A Practical Guide to LLM Sampling
Learn how temperature, top-k, and top-p settings control LLM output, with practical recipes and pitfalls for tuning chatbots, coding assistants, and RAG systems.
2423 wordsRead articleReAct Explained: How AI Agents Combine Reasoning with Real-World Actions
Learn how the ReAct framework blends reasoning and tool use to power AI agents, and how it differs from Chain-of-Thought, RL, and reasoning models.
1389 wordsRead article
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