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Lesson 1: Coding Has Changed: Working With AI as a Developer

If an AI agent can inspect a repository, plan a feature, change files and run tests, what is the developer actually responsible for? Open Full Article...

Lesson 2: RTF: The Foundation of a Good AI Request

What could an AI reliably know from “Fix my code”? Open Full Article...

Lesson 3: When RTF Is Not Enough: Building a Complete Prompt

Why can “Act as a Python developer, add a login function, and return the code” sound perfectly clear yet still produce the wrong result? Open Full Article...

Lesson 4: Goals, Requirements and Specifications

Fix the login system sounds urgent, but what exactly is broken? Open Full Article...

Lesson 5: Context Engineering: Giving the AI What It Needs

Why might the same AI produce three different fixes for the same bug? Open Full Article...

Lesson 6: Limits, Boundaries and Protecting Existing Code

What if an AI fixed an error perfectly by deleting the feature that caused it? Open Full Article...

Lesson 7: Format, Audience and Communicating With the Model

A small coding problem can produce five different answers: a full source file, JSON, a test report, an implementation plan or a plain explanation for… Open Full Article...

Lesson 8: Project Instructions and Persistent Context

What if a developer had to remind an AI coding agent about the same framework, folder layout, testing method and coding rules every morning? Open Full Article...

Lesson 9: Getting AI to Read and Investigate Before Coding

The login is broken—replace the code. Open Full Article...

Lesson 10: Working Effectively in Long LLM Conversations

After hours of work, a developer’s AI conversation contains the original feature request, abandoned approaches, a corrected database assumption, an… Open Full Article...