This course centers on Claude Code's production-grade workflow, helping developers shift from relying on AI-generated code to methodically and rhythmically driving AI coding tools. It starts with LLM mechanics and context-window management, then dives into the plan/execute/clean loop, writing AGENTS.md and custom skills, multi-stage planning and the tracer-bullet method, and building automated feedback loops. You'll master testing, pre-commit hooks and red-green refactoring, and learn how the Ralph loop lets Claude Code run autonomously against a GitHub Issues todo list. It offers async releases and multi-timezone live Q&A, with all materials kept for repeated review.

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• 1.Course Intro to Production-Grade Workflow
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• 2.LLM Mechanics Basics
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• 3.Why the Context Window Matters
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• 4.Managing Context Without Losing Control of the Codebase
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• 5.Building the Right AI-Coding Workflow
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• 6.Using Claude Code as a Pro Tool, Not a Crutch
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• 7.Avoiding Spaghetti Code Chaos
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• 8.Async Learning & Live Q&A Explained
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• 9.Keeping & Using the Materials
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• 10.Plan/Execute/Clean Loop Explained
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• 11.Keeping Claude Code at Peak Performance
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• 12.Writing the AGENTS.md File
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• 13.Custom Skills to Guide Output
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• 14.Token-Saving Prompt Strategies
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• 15.Core Workflow Hands-On Drill
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• 16.Common Workflow Pitfalls & Fixes
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• 17.Codebase-Control Training
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• 18.Stage Review & Optimization
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• 19.How to Make Multi-Stage Plans
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• 20.The Core Idea of the Tracer-Bullet Method
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• 21.Handling Scale Beyond a Single Agent Session
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• 22.Large-Feature Breakdown & Planning Practice
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• 23.Progress Control During Execution
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• 24.Keeping Context in Large-Feature Dev
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• 25.Staged Delivery & Verification
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• 26.Decisions & Adjustments in Complex Scenarios
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• 27.Large-Feature Planning Case Study
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• 28.Building the Automated Feedback Loop
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• 29.TDD & AI Collaboration
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• 31.Configuring & Using Pre-Commit Hooks
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• 32.Red-Green Refactoring in Practice
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• 33.Catching Errors Before They Pile Up
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• 34.Automated Testing & CI
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• 35.Feedback-Loop Efficiency Tuning
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• 36.Automated Feedback Comprehensive Drill
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• 37.Ralph-Loop Principles & Use Cases
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• 38.Running Against a GitHub Issues Todo List
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• 39.Letting Claude Code Handle Tasks Autonomously
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• 40.Safety Boundaries & Monitoring for Autonomous Runs
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• 41.Ralph-Loop Hands-On Config
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• 42.Verifying & Merging Autonomous Results
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• 43.Production-Grade Code Quality Control
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• 44.Full Workflow Chain & Summary
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• 45.Continued Learning & Resource Picks
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💡 Study tip: This tutorial reflects industry practices at the time of release; please align it with the latest platform rules while learning.