PromptMap emerges as a game-changing open-source tool that generates AI-friendly structural blueprints of .NET projects. By mapping namespaces, classes, and methods into digestible hierarchies, it equips ChatGPT and similar assistants with critical context—eliminating manual code sharing and boosting development productivity.

The Context Gap in AI-Assisted Development

As AI coding assistants like ChatGPT become indispensable co-pilots for developers, a critical challenge persists: How do you efficiently convey complex project structures to an AI? Pasting individual files is cumbersome, while describing architecture manually is error-prone. Enter PromptMap—a .NET console application that automates contextual mapping for seamless AI collaboration.

How PromptMap Bridges the AI-Developer Divide

PromptMap scans Visual Studio .sln files or directories of .cs files, generating a structured hierarchy of namespaces, classes, methods, and properties. Unlike traditional documentation tools, its output is optimized for AI consumption—concise yet comprehensive. Key features include:

Recursive directory/solution parsing
Configurable inclusion of private members and constructors
Output to file or stdout
Zero external dependencies (runs entirely locally)

Developers launch it via CLI:

promptmap --solution MyProject.sln --include-private --out ai_context.txt

The Technical Engine: Roslyn-Powered Analysis

Under the hood, PromptMap leverages Microsoft's Roslyn compiler platform to analyze C# syntax trees. The RoslynWalker class processes semantic models to build a Node-based representation of the codebase. This tree structure preserves relationships between entities while filtering noise—critical for keeping AI prompts focused.

Example output for a UserService class:

MyApp.Services
└─ UserService
   ├─ Method User GetById(int id) [public]
   ├─ Method void Update(User user) [public]
   └─ Property ILogger Logger { get; } [private]

Why This Changes the Game for .NET Developers

Precision in AI Queries: Feeding PromptMap's output to ChatGPT yields more accurate refactoring suggestions, bug fixes, and feature implementations—the AI "sees" your entire architecture.
Onboarding Acceleration: New team members (human or AI) grasp project structure in seconds.
Security Compliance: With no cloud dependencies, proprietary code never leaves your machine.

As AI coding tools evolve, solutions like PromptMap highlight a paradigm shift: The future isn't just about smarter AIs—it's about smarter context handoffs between humans and machines. By automating this bridge, developers reclaim hours previously lost to manual context-setting, pushing more focus to creative problem-solving.