PlanToCode
AI path verification

Stop AI from generating wrong file paths

AI tools hallucinate import paths, reference non-existent files, and break your build with phantom dependencies. PlanToCode verifies every file path before execution, eliminating hallucinated references in monorepos and legacy codebases.

The wrong file path problem

"AI tried to import from a non-existent path"

You ask AI to refactor a component. It confidently generates code that imports from@/components/ui/NewButton— a file that does not exist. Your build fails. You waste 20 minutes tracking down the phantom import.

AI-generated code (broken)
// AI suggests this import
import { Button } from '@/components/ui/NewButton';
import { Dialog } from '@/shared/dialogs/ConfirmDialog';
import { useAuth } from '@/hooks/useAuthentication';

// But these files don't exist in your codebase!
// ❌ @/components/ui/NewButton
// ❌ @/shared/dialogs/ConfirmDialog
// ❌ @/hooks/useAuthentication
After PlanToCode verification (correct)
// PlanToCode verifies and corrects paths
import { Button } from '@/components/ui/button';
import { Dialog } from '@/components/dialogs/confirm-dialog';
import { useAuth } from '@/lib/hooks/auth';

// ✓ All paths verified against actual filesystem
// ✓ Proper monorepo path resolution
// ✓ No hallucinated imports

Monorepo nightmares

AI confuses @workspace/core with@workspace/shared, generating imports that look plausible but reference the wrong package.

Legacy code confusion

Your codebase has src/components andlib/components. AI picks the wrong one, or hallucinates a third directory that never existed.

Context window limits

Large projects exceed AI context windows. The model guesses at file locations based on incomplete information, producing confident but incorrect path references.

Why AI generates wrong file paths

Model hallucination

Large language models are trained on millions of codebases with different structures. When generating code, they pattern-match against training data, not your actual filesystem.

  • •Models predict probable paths, not actual paths
  • •Training data contains inconsistent naming conventions
  • •No filesystem verification in the generation loop

Context window overflow

Even 200K token context windows cannot hold entire enterprise codebases. The model sees a fraction of your files and infers the rest, leading to path mismatches.

  • •Incomplete directory tree visibility
  • •Missing import alias configurations
  • •Outdated snapshots of evolving codebases

Monorepo complexity

Monorepos amplify path confusion with workspace protocols, internal packages, and multiplenode_modules directories.

  • •Conflicting package names across workspaces
  • •Complex tsconfig path mappings
  • •Hoisted dependencies with unclear resolution

No verification layer

Standard AI code generation has no post-processing step to validate file existence. Generated code goes directly to you without filesystem checks.

  • •No file existence validation
  • •No import path resolution testing
  • •No cross-reference with actual directory structure

How PlanToCode prevents wrong file paths

Pre-execution file discovery

Before AI generates any code, PlanToCode runs a 5-stage file discovery workflow that maps your actual filesystem. Every file path is verified to exist before being included in context.

  • Scans repository with git ls-files integration
  • Validates file existence against actual filesystem
  • Resolves symbolic links and path aliases
  • Normalizes paths for cross-platform compatibility
Learn about file discovery

Path validation pipeline

Stage 5 of the discovery workflow specifically validates and corrects file paths. This happens automatically before any AI model sees your codebase.

  • Checks file accessibility and permissions
  • Resolves path inconsistencies automatically
  • Handles monorepo workspace protocols
  • Validates import alias mappings from tsconfig
Technical documentation

Monorepo-aware resolution

PlanToCode understands monorepo structures and correctly resolves workspace references, internal package paths, and hoisted dependencies.

  • Parses workspace configurations (pnpm, yarn, npm)
  • Resolves internal package cross-references
  • Handles multiple node_modules hierarchies
  • Detects and respects workspace protocol imports

Real-time verification feedback

Watch file discovery progress in real-time with stage-by-stage updates. See exactly which paths are being validated and corrected before AI generates code.

  • Live progress tracking for each discovery stage
  • Detailed error messages for path issues
  • File count and token usage estimates
  • Cost tracking: typically $0.10-0.15 per workflow

File discovery in action

1
Repository scan starts
Scanning repository: /Users/dev/myapp
Root directory structure (2 levels):
  ├── packages/
  │   ├── core/
  │   ├── shared/
  │   └── ui/
  ├── apps/
  │   ├── web/
  │   └── mobile/
  └── libs/
      └── utils/

Selected roots: packages/core, packages/shared, apps/web
2
Regex filtering with git integration
Running: git ls-files --cached --others --exclude-standard
Generated pattern: \.(tsx?|jsx?|json)$
Files matched: 847
After binary filtering: 612
Respecting .gitignore: 589 final candidates
5
Path validation and correction
Validating discovered paths...
✓ packages/core/src/auth.ts → exists, accessible
✓ packages/shared/utils/format.ts → exists, accessible
✓ apps/web/components/Button.tsx → exists, accessible
✗ packages/ui/components/Modal.tsx → does not exist
  → corrected to: packages/shared/components/modal.tsx
✓ All paths validated and normalized
✓ Monorepo workspace references resolved
Discovery complete

All 589 files verified against filesystem. AI will only reference validated paths in generated code. No hallucinated imports possible.

Cost: $0.12 | Duration: 23s | Tokens: 47,384

Real-world scenarios

✗
Without path verification

You are refactoring authentication logic in a Next.js monorepo. You ask AI to update the login component. AI confidently generates imports from @/lib/auth/session,@/components/forms/LoginForm, and@/hooks/useAuthState.

You copy the code into your editor. TypeScript immediately shows red squiggles. None of these paths exist. The actual paths are @workspace/auth/session,@workspace/ui/forms/login, and@workspace/hooks/auth-state.

You spend 30 minutes manually correcting import paths, checking each one against your file tree, and discovering that some files have been renamed or moved. The AI-generated logic is correct, but the path hallucinations make it unusable. Your velocity drops as you become a path-correction specialist instead of a developer.

✓
With PlanToCode verification

You open PlanToCode and run file discovery on your authentication workspace. The 5-stage workflow scans 589 files in 23 seconds, validating every path against your actual filesystem. It detects your monorepo structure, parses workspace configurations, and resolves all internal package references.

Now you ask AI to refactor the login component. PlanToCode feeds the AI only verified paths. The generated code imports from @workspace/auth/session,@workspace/ui/forms/login, and@workspace/hooks/auth-state— all correct on first generation.

You copy the code and paste it into your editor. Zero TypeScript errors. The refactor works immediately. You ship the feature in minutes instead of hours. File discovery cost $0.12. Time saved debugging phantom imports: priceless. This is how AI-assisted development should work.

How PlanToCode compares

FeatureStandard AI ToolsPlanToCode
Pre-execution path validation✗✓
Monorepo workspace resolution✗✓
Git integration with .gitignore respectPartial✓
TypeScript path alias resolution✗✓
Real-time verification progress✗✓
Symbolic link resolution✗✓
Cost-optimized token batchingBasic✓ Advanced
Binary file filteringBasic✓ 97 types

Comparison based on Claude Code, Cursor AI, GitHub Copilot, and other leading AI code assistants as of January 2025.

Start preventing wrong file paths

1

Download PlanToCode

Install for macOS, Windows, or Linux. Connect to your preferred AI model (Claude, GPT-4, Gemini, or local models). No API keys required to start.

2

Run file discovery

Open your project and start the file discovery workflow. PlanToCode automatically scans and validates your entire codebase structure. Watch real-time progress for each stage.

3

Generate verified code

AI generates code using only verified file paths. No hallucinated imports, no phantom dependencies, no broken builds. Copy with confidence.

The impact of verified file paths

For individual developers

  • Stop debugging phantom imports. No more hunting through directory trees to find the correct path AI should have generated in the first place.
  • Ship faster. When AI-generated code works on first paste, your velocity increases dramatically. Focus on logic, not path corrections.
  • Trust AI output. Path verification builds confidence. You know imports are correct before you copy code to your editor.
  • Work with legacy code. Complex, undocumented codebases become navigable when every path is verified against reality.

For engineering teams

  • Onboard faster. New team members understand codebase structure through verified file discovery. No guessing at import conventions.
  • Reduce PR noise. Eliminate commits that solely fix incorrect import paths. Code reviews focus on logic, not path corrections.
  • Scale monorepos confidently. As your workspace grows, path verification prevents the exponential increase in import confusion.
  • Standardize AI usage. When everyone uses verified paths, AI-generated code maintains consistency across the team.

Measured impact

85%
Reduction in path-related debugging time
$0.12
Average cost per verification workflow
23s
Average scan time for 589 files

Based on internal testing with monorepo codebases ranging from 200 to 5,000 files. Your results may vary depending on repository structure and complexity.

Frequently asked questions

Does file discovery work with monorepos?

Yes. PlanToCode explicitly supports monorepo structures including pnpm workspaces, Yarn workspaces, npm workspaces, and Nx. The file discovery workflow parses workspace configurations and correctly resolves internal package references, hoisted dependencies, and workspace protocol imports.

How much does file discovery cost per run?

Typically $0.10-0.15 per complete workflow, depending on repository size and complexity. The 5-stage discovery process uses intelligent token batching and content-aware estimation to minimize API costs while maximizing accuracy. Cost tracking is built into every stage.

Can I see which paths were verified?

Absolutely. PlanToCode provides real-time progress tracking with stage-by-stage updates. You can see exactly which directories were scanned, how many files passed validation, which paths were corrected, and the final list of verified files available to AI.

Does this work with TypeScript path aliases?

Yes. The path validation stage understands tsconfig.json path mappings and resolves TypeScript aliases like @/* or~/* against your actual filesystem. This ensures AI-generated imports use correct alias syntax.

What happens if AI still suggests a wrong path?

While file discovery dramatically reduces path hallucination, AI models can still generate new file names for code you are creating. PlanToCode focuses on verifying existing files. For newly created files, the implementation plan shows clear file paths and you can validate them before copying to your AI tool.

Can I exclude certain directories from discovery?

Yes. The file discovery workflow respects .gitignore rules automatically. Additionally, binary files and 97 common non-code extensions are filtered by default. You can also configure custom exclusion patterns in your project settings.

Stop fighting hallucinated file paths

AI should reference files that actually exist. PlanToCode verifies every path before code generation, eliminating phantom imports and broken builds. Perfect for monorepos, legacy codebases, and complex project structures.

Deep dive: File discovery systemRead technical documentation