A complete honest walkthrough of building a URL shortener API from scratch using Claude Code — including the prompts that worked, the ones that failed, the corrections needed, and everything that shipped in 4 hours.

Developer-specific prompt engineering: constraint-first prompts, chain-of-thought for complex algorithms, few-shot examples for code style, structured JSON output, and the 10 anti-patterns that produce tutorial code instead of production code.

Having a 200K context window does not mean dumping 200K tokens in and getting great answers. Context placement, chunking strategy, compression, and retrieval order all affect LLM output quality dramatically.

RAG, fine-tuning, and prompting are not competing techniques — they solve different problems. RAG handles dynamic knowledge. Fine-tuning improves style and format. Prompting shapes behavior. Here is when to use each for developer tools.

A GitHub Actions bot powered by Claude reviews every PR for security vulnerabilities, performance issues, missing tests, and code style — posting specific line comments before any human reviews it.

Cursor, Copilot, and Claude Code each win on different tasks. Cursor dominates codebase-aware editing. Copilot wins for inline completions in VS Code. Claude Code excels at autonomous multi-file tasks. This comparison uses real benchmarks.

Multi-agent systems assign each task to the model best suited for it — Claude for long-context reasoning, GPT-4o for vision, fine-tuned models for domain tasks. Routing logic, handoff protocols, and cost optimization strategies.

Claude generates production-ready code when you give it the right system prompt, structured output format, and quality constraints. These patterns turn Claude into a reliable code generation engine for real projects.

Vibe coding is not letting Cursor write your entire app while you describe vibes. Senior developers use AI as a force multiplier for specific tasks — scaffolding, tests, boilerplate, and documentation — while retaining ownership of architecture.

Most AI agent tutorials stop at a simple tool-calling loop. Production agents need planning/execution separation, persistent memory, error recovery, and observability. This is the real architecture.