AI code assistant comparison that actually accounts for enterprise C# codebases and not just greenfield demos
Every AI tool comparison I see online uses greenfield demo projects. "Look, I told it to build a REST API and it generated the whole thing!" Great. That tells me nothing about how the tool performs in a 5-year-old C# codebase with 800k lines, DDD architecture, custom middleware, 15 internal NuGet packages, and entity framework migrations dating back to EF Core 2.
I've been running an evaluation on our actual production codebase and the results are very different from what the YouTube demos suggest. The elephant in the room is context. Our codebase has patterns that evolved over 5 years. We have custom Result types, specific repository patterns, our own middleware pipeline, domain events that follow a particular convention. No AI tool knows about any of this out of the box.
Tools that just look at the current file generate generic C# that compiles but doesn't fit. They suggest using standard exception handling when we use Result monads. They generate repositories that don't follow our interface pattern. They create controllers that bypass our custom middleware.
The tools that try to understand your codebase (by indexing repos or connecting to docs) are meaningfully better for enterprise C#. Not perfect, but the gap between "knows your codebase" and "doesn't know your codebase" is the biggest differentiator I've found. Bigger than model quality, bigger than suggestion speed, bigger than chat features.
For teams evaluating AI tools for established C# codebases, how much weight are you putting on the tool's ability to learn your specific codebase patterns?