The Context Illusion: Why LLMs Don't Know Your Code Like You Think They Do

A few weeks ago, I was poking around a file I hadn't touched in a while. I typed a few lines, and my AI assistant filled in the rest—neatly matching the project's structure, naming conventions, and even suggesting the right API call.

It felt… remarkable.

But when I ran the test suite, two broke. The assistant had reused a pattern from an earlier file that had since been deprecated. The logic looked right—but it wasn't. And more importantly, the AI had no way of knowing why that old pattern had been replaced in the first place.
This keeps happening. And it raises a deeper question:

What does your AI assistant actually know about your codebase?

And what doesn't it know?

The Illusion of Understanding

AI tools like Copilot, Claude, Cursor, and ChatGPT are getting strikingly good at predicting what we want to write. With just a few hints, they mimic your patterns, suggest helpful completions, and even pull in the right utilities.
It feels like they understand your project.

But what's really happening is high-confidence pattern recognition—not actual comprehension. The assistant doesn't know your migration plan, your unwritten team conventions, or the risky parts of the repo you silently avoid. It's autocomplete on steroids—not a teammate with context.

What These Tools Actually See

Let's demystify what most LLM-based code assistants can access:

• The current file (or maybe a few open ones)
• A limited amount of surrounding or linked code
• Sometimes: an embedded snapshot of your repo via chunking/indexing
• Usually: a fixed-size context window (e.g. 100K–200K tokens) Even the best assistants are only seeing a slice of your codebase at any given time. And unless you've built custom memory, they forget everything between sessions. In practice, it's a bit like asking a contractor to finish remodeling your house… after showing them one photo of the kitchen.

Why This Matters

The context gap isn't just annoying—it creates real problems:

The Velocity Paradox: Teams using AI assistants without proper context actually ship slower. You save 10 minutes on initial coding, then spend 2 hours debugging why the AI used the old authentication pattern that was deprecated last month.

Technical Debt Acceleration: AI tools amplify existing architectural problems. They see legacy/ and new/ folders and assume both are valid, reinforcing the split instead of helping you migrate.

Onboarding Friction: New team members + AI assistants = double context gap. The AI suggests patterns the new hire doesn't understand, and the new hire doesn't know enough to question them.

The Context Hierarchy

Not all context is created equal. Here's what matters most:

Level 1: Code Patterns (naming, structure, conventions)

• Easy for AI to learn, often handled well

Level 2: Business Logic (domain rules, edge cases)

• The assistant suggested using useQuery from React Query v3, but we migrated to v4's useSuspenseQuery last month

Level 3: Architectural Decisions (why we chose X over Y)

• It sees the function signature, not the performance regression that forced us to replace it

Level 4: Team Dynamics (who owns what, review preferences)

• Maybe everyone avoids Object.assign, or wraps third-party APIs for logging—but that's tribal knowledge

Level 5: Historical Context (past failures, migration states)

• Half your project is using v2/clients, half isn't. The model can't tell.

Context Debt

Every undocumented decision creates future context debt. AI tools accelerate this accumulation:

• Pattern drift: The assistant sees 3 different ways to handle errors and picks the most common one—which happens to be the oldest and most problematic
• Decision decay: Why did we choose this database? The AI doesn't know about the performance issues that led to the migration
• Knowledge silos: The person who knows why this code is structured this way left the team, and the AI has no way to access that reasoning Teams need "context refactoring" sessions, not just code refactoring. --- ## The Risk: High-Confidence Mistakes The problem isn't just that these tools make mistakes—it's that they do so with remarkable fluency. Because the code looks correct and reads cleanly, we're less likely to challenge it. Especially when it saves time. And that's exactly how subtle bugs slip through:
• Reintroducing a logic bug that was patched six months ago
• Suggesting a refactor that splits shared logic without realizing it's used in tests
• Using deprecated APIs that still "work," but are no longer safe or supported These aren't wild hallucinations—they're plausible errors. The kind you don't notice until production. --- ## Real Solutions Here's what actually works: Document Architectural Decisions

# DECISIONS.md 2024-01-15: Replaced useQuery with useSuspenseQuery - Why: Better error boundaries, cleaner loading states - Migration: 60% complete, avoid mixing patterns - Files to avoid: legacy/auth/hooks.ts 

Context-Aware Tool Configuration

# .cursorrules - Don't suggest Object.assign (team preference) - Avoid modifying files in /config/ (sensitive) - Use v2/clients for new API calls (migration in progress) - Check DECISIONS.md before suggesting major changes 

Weekly Context Sync
15 minutes to update AI tools on recent changes:

• New patterns adopted
• Deprecated code removed
• Performance issues discovered
• Team preferences evolved Context Annotations

// @context: This pattern was deprecated due to edge case in v2 // @context: Performance critical - don't refactor without benchmarks // @context: Used by tests in /integration/ - check before moving 

Final Thought

LLMs are powerful collaborators, but they face the same challenge we do: they need context to be effective. Without the right information, they're working with fragments—just like we would be if we jumped into an unfamiliar codebase.
The difference is that we can ask questions, dig through documentation, or reach out to teammates. Our AI assistants need us to provide that context proactively.
So if you're going to code with an assistant, remember:

Context is everything.

The more you share, the better they can help.

How are you dealing with context gaps in AI tooling? I'd love to hear what's worked—and what hasn't.