INSTRUCTIONS Still Matter
Intentional Context Makes All The Difference
I was debugging some Python code last week when Claude started acting like it had lost a good portion of its intelligence.
Wrong files. Missing the obvious application flow. Simple requests that usually worked in under a minute suddenly took fifteen minutes of back-and-forth.
Then it hit me—I'd accidentally deleted my Claude.md files during a repository cleanup.
Rookie mistake. But it reminded me of something important: even as these AI coding tools get dramatically better at reading context from source code, intentional instructions still make a massive difference.
In my case, it was literally night and day.
What I'd Lost (And Why It Hurt)
Here's the instruction stack that actually works:
instructions.md contains general guidelines for any AI coding assistant working on my projects. Coding standards, architectural preferences, testing approaches, behavioral expectations. The foundational "how we work here" document.
workflow.md gets specific about process. My Git workflow preferences, how I use GitHub issues for tracking work, branch naming conventions, commit message standards. When Claude knows I prefer feature branches with specific naming patterns, it suggests the right commands without me having to specify every time.
project.md provides the strategic context that source code can't capture.
The project's intent, key architectural decisions, business constraints, how different components relate to broader goals. This is where I document the "why" behind technical choices.
The difference between having these files and not having them isn't subtle. With proper instructions, Claude immediately understood my Python FastAPI structure, knew to check my custom service patterns before suggesting generic solutions, and followed my preferred patterns for Pydantic models and exception handling.
Without them? Every interaction required extra context-setting. We're talking about going from 30-second code suggestions to 5-minute explanation sessions.
What I didn't realize until I started digging into this is how this challenge has become the defining feature across the entire AI coding landscape.
How Everyone Else is Solving This
Turns out my experience is part of a much larger story.
Every major AI coding tool is grappling with this same fundamental challenge: how do you give AI the right context to be genuinely helpful rather than just impressive?
Cursor has evolved way beyond simple .cursorrules files. They now use a sophisticated .cursor/rules/ directory structure with MDC format that supports hierarchical organization. For monorepos, you can have backend/.cursor/rules/ and frontend/.cursor/rules/ with different scoped rules.
Their system automatically attaches rules based on glob patterns. It can even generate "memories" from chat conversations. Pretty clever.
Windsurf took a completely different approach. Instead of requiring manual file creation, their "Cascade Memories" system automatically remembers important project context across sessions. When you're working on a feature, Windsurf learns your patterns and applies them without you having to specify anything.
It's the closest thing to AI that "just knows" what you want.
GitHub Copilot uses .github/copilot-instructions.md files for repository-wide context, plus they've added @workspace functionality that automatically understands entire project structures. Their latest update includes multi-model support and something called "Copilot Spaces" for persistent organizational context.
Continue.dev might have the most flexible approach. They support .continuerc.json for project-specific overrides, plus a config.ts file for programmatic customization. You can build custom context providers and integrate with external tools through the Model Context Protocol.
Aider handles this through comprehensive repository mapping using tree-sitter integration. It creates dependency graphs, extracts symbols with full signatures, and uses PageRank-style relevance scoring. When you ask Aider to work on something, it automatically understands which files are related and why.
The interesting pattern? Every tool is converging on the same core insight: context is everything.
But they're implementing it in dramatically different ways based on their philosophy about human-AI collaboration.
Why Context Still Beats Code-Reading
Modern AI tools have gotten impressive at analyzing codebases. They can infer patterns, understand dependencies, make reasonable assumptions about project structure.
But inference isn't instruction.
When Claude reads my code, it can see that I use Python with FastAPI and SQLAlchemy. When it reads my instructions, it knows I prefer Pydantic models for all data validation, how I structure my service layers, and that I always want proper exception handling around database operations.
The code shows what I've done. The instructions explain how I want to work.
This becomes especially important for:
Architectural consistency (ensuring new features follow established patterns)
Tool preferences (using the specific libraries and approaches I've standardized on)
Workflow integration (creating branches, commits, and PRs that fit my process)
Business context (understanding constraints and priorities that aren't visible in code)
The performance difference is measurable across the industry. Users report 25-40% productivity improvements when tools have proper context versus relying on code inference alone. Cursor users see 40% higher acceptance rates for context-aware suggestions.
That gap between "smart" and "contextually smart" is significant.
What's fascinating is watching different tools solve this problem. Amazon Q Developer (evolved from CodeWhisperer) shows full transparency about which files inform its responses. Replit AI uses what they call a "Society of Models"—multiple specialized models that share context from file structure, chat history, and program output.
Pieces for Developers goes furthest with their Long-Term Memory Engine, capturing 9+ months of workflow context across all applications.
But here's the fundamental insight: the best AI assistants aren't just reading your code—they're understanding how you work.
The Standards War (And Why It Matters)
Something important is happening in the industry: standardization around context management.
The Model Context Protocol (MCP) has emerged as what developers are calling "the USB-C of AI apps." It's now officially adopted by OpenAI and Google DeepMind, with 5,000+ active servers providing standardized ways to connect AI tools with external data sources.
Here's why this matters for you. Your investment in context and instructions is becoming more portable. Continue.dev, JetBrains AI, and Claude Code are leading MCP adoption with integrations to Jira, Slack, and Figma. When you set up context providers through MCP, you're not locked into a single tool's ecosystem.
The other trend worth watching is the split between automatic and manual context management:
Automatic leaders like Windsurf and Amazon Q Developer are pushing toward AI that requires minimal configuration. Windsurf's Cascade system handles multi-file editing with full repository awareness by default. Q Developer's workspace indexing stops at memory limits to prevent overhead but incrementally updates when files change.
Manual control champions like Cursor and Continue.dev offer granular control for developers who want to precisely manage what AI sees. Cursor's @-symbol system (@Files, @Folders, @Code, @Codebase) gives you surgical precision. Continue.dev enables custom context providers through modular configuration.
There's no "right" answer here—it depends on your workflow and team needs.
But understanding these philosophical differences helps you choose tools that match how you actually work.
Setting Up Your Own Context Stack
Creating effective instruction files doesn't require extensive documentation. Here's what works:
Start with instructions.md covering your essential coding standards. Include your preferred libraries, testing approaches, and any specific patterns you want consistently applied. I include sections on error handling, state management, and API integration patterns.
Add workflow.md for process specifics. Document your Git workflow, issue tracking system, and any automation you've set up. If you have specific preferences for commit messages or branch naming, include those.
Create project.md for strategic context. Explain what you're building, key architectural decisions, and any business constraints that affect technical choices. This is especially valuable for projects with unusual requirements or legacy considerations.
Keep these files updated as your preferences evolve.
When you establish a new pattern or change your approach to something, update the relevant instruction file. The small maintenance overhead pays massive dividends in AI assistance quality.
The Compound Effect
What surprised me most about losing these files wasn't just the immediate productivity hit.
It was how the quality degradation compounded.
When Claude had to guess about my preferences, it made reasonable but incorrect assumptions. Those assumptions led to suggestions that didn't fit my architecture, which required corrections that led to more context-setting.
With proper instructions, we skip that entire cycle. Claude starts with the right context, suggests appropriate solutions, and builds on established patterns. The time savings add up quickly, but more importantly, the suggestions get better because they're grounded in actual project requirements rather than general best practices.
Here's What I Think Happens Next
Even as AI tools continue improving their ability to read and understand code, intentional instruction remains the difference between a helpful assistant and an effective collaborator.
The tools are getting smarter, but they can't read our minds about workflow preferences and strategic context.
What's become clear from looking across the industry is that every successful AI coding tool is solving this same problem—they're just doing it in different ways. Some bet on automatic context detection, others on manual control, and the smartest ones are finding ways to blend both approaches.
My deleted instruction files reminded me of something fundamental: the quality of AI assistance is directly proportional to the quality of context you provide. Whether that context comes from Cursor's rules, Windsurf's memories, Aider's repository maps, or your own carefully crafted instruction files, the principle remains the same.
Your instruction files—whatever form they take—are force multipliers.
The few hours you invest upfront in documenting your patterns, preferences, and project context will pay dividends in every AI interaction moving forward. As these tools get more sophisticated, the ones that understand your specific way of working will become increasingly valuable.
The industry is converging on context-aware AI assistance, but the developers who take time to intentionally structure their context today will be the ones who get the most out of these tools tomorrow.
Interested in my instructions files? Hit me up in the comments.