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.

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.

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.

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.

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.

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.

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.

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.