Better Context.
Better Code.

Context engineering is the discipline of curating the entire information environment an AI agent operates within -- not just the prompt text, but the files it reads, the rules it follows, the conversation history it carries, and the tools it can access. Shopify CEO Tobi Lutke popularized the term in mid-2025, and Andrej Karpathy endorsed it immediately. The key insight is that a perfect prompt with bad context produces bad code, while even a simple prompt with precisely curated context produces excellent code. Martin Fowler defines it as "curating what the model sees so that you get a better result." This is now considered the core skill separating productive AI-assisted developers from those getting expensive autocomplete.

A .cursorrules file (or the newer .cursor/rules directory) is a configuration file that acts as a persistent system prompt for Cursor AI. It tells Cursor your coding conventions, preferred libraries, architectural patterns, file naming standards, and anti-patterns to avoid. Think of it as encoding your team's code review checklist into a file that AI reads before every interaction. For Claude Code, the equivalent is CLAUDE.md. For GitHub Copilot, you use custom instructions in the repository settings. The course teaches you how to write effective rules files for each tool and how to structure them so they scale across teams without becoming stale.

No. While Cursor provides the most advanced context indexing, the principles transfer to every AI coding tool. Context control techniques apply to ChatGPT (using project knowledge and file uploads), Claude Projects (using project documents), GitHub Copilot (using custom instructions and @workspace), and Claude Code (using CLAUDE.md and project context). The underlying skill is the same: deciding what information the AI needs, what information creates noise, and how to structure your project so AI tools can find the right files automatically.

AI models have an "attention budget." Even with a 200K or 2M token context window, the model cannot reason about every token with equal depth. When you dump your entire repository into context, the model must decide which parts are relevant -- and it often guesses wrong. Irrelevant code creates "noise" that competes with the "signal" of the files that actually matter. This leads to hallucinations where the AI references patterns from unrelated files, uses outdated API versions found elsewhere in the repo, or generates code that follows the wrong file's conventions. Selective context injection -- providing only the 2-3 files directly relevant to the task -- produces dramatically better results.

Legacy codebases are the hardest context challenge because they contain outdated patterns, dead code, and inconsistent conventions that confuse AI. The course teaches a "Context Extraction" technique: instead of giving AI the raw legacy files, you create a minimal interface description that captures what the legacy code does (its inputs, outputs, and side effects) without exposing how it does it. This prevents AI from mimicking outdated patterns while still giving it enough information to integrate with the existing system. For gradual modernization, we teach how to use .cursorrules to define "new code" standards that AI follows even when surrounding code uses older patterns.

Context stripping is the technique of removing boilerplate, comments, import statements, and irrelevant code from the files you provide to AI, leaving only the logic that matters for the current task. Use it when you hit token limits or when AI output quality degrades. For example, a 500-line React component might have 200 lines of imports, type definitions, and JSX boilerplate. If you need AI to fix the state management logic, strip everything except the state declarations, the relevant handlers, and the effect that is misbehaving. The course teaches specific stripping patterns for React, Laravel, Node.js, and Python codebases.

When you use @codebase in Cursor or ask Claude Code to find files, the tool performs retrieval-augmented generation (RAG): it searches an index of your codebase to find files that seem relevant to your query, then includes those files in the AI context. The quality of the output depends entirely on how well the tool retrieves the right files. The course teaches you how to "steer" this retrieval: structuring your project so related code lives in predictable locations, naming files and functions descriptively so they match semantic search queries, and using explicit file references (@file) when you know exactly what AI needs instead of relying on automatic retrieval.