AI for .NET Developers: Coding, Debugging, and Review

AI coding assistants have become a standard part of .NET development work. They generate boilerplate, explain unfamiliar APIs, suggest fixes for compilation errors, and review code for common problems faster than reading documentation. The gains compound quickly; so do the risks if a suggestion from an older training dataset introduces a deprecated pattern or an insecure call.

The context that makes AI suggestions accurate for your project, the .NET version, the architecture decisions, the library choices, should be available to every session. A tool like fenbs keeps that context outside any single AI app, so it survives when you switch tools or start a new conversation.

What AI coding assistants do well for .NET projects

For .NET developers, the strongest AI use cases cluster around two things: writing code that follows a known pattern, and explaining code you did not write. Both play to what models do well: pattern matching on a large corpus of C# and .NET code.

• Boilerplate generation: repository classes, service registrations, controller actions, DTOs, unit test scaffolding.
• LINQ query construction: translating a described filtering or projection requirement into a correct LINQ expression.
• Exception handling patterns: generating try/catch structures for specific exception types with appropriate logging.
• Code explanation: reading unfamiliar legacy code and summarizing what it does, including the intent of non-obvious patterns.
• Refactoring suggestions: identifying switch statements that could be replaced with polymorphism, or LINQ chains that could be simplified.

Version-specific guidance and the training data gap

This is the most important limitation for .NET developers to understand. AI models are trained on code up to a knowledge cutoff. If you are working on .NET 9, a model may suggest patterns from .NET 6 or even .NET Framework. Minimal APIs, top-level programs, source generators, and new C# language features are all areas where outdated suggestions appear regularly.

The practical mitigation: always tell the model the target framework (net9.0, net8.0), the C# version, and any relevant NuGet packages with their version numbers. Ask the model to flag if it is uncertain about the current API for the version you specified. Then verify any unfamiliar API call against the official Microsoft documentation at docs.microsoft.com↗ (opens in a new tab) before merging.

For AI developer experience tools and a broader comparison, see the Microsoft AI tools overview. For specialized coding agents, solo developer AI coding agents covers the agentic end of the tooling landscape.

Debugging workflows with AI

AI is most useful in debugging when you give it a precise description of the problem: the error message and stack trace, the relevant code, the expected behavior, and what you already tried. A vague "why is this broken?" produces a vague list of possibilities. A precise description plus the stack trace usually produces a targeted explanation in the first response.

For NullReferenceException, async/await ordering issues, and common EF Core query problems, AI explanations are usually accurate. For problems that involve specific version behavior, race conditions, or distributed system edge cases, verify the explanation against your own testing before trusting it.

One useful pattern: ask the model to explain line by line what a piece of code does before you ask it what is wrong. Understanding the model's reading of the code often reveals whether it understands the context correctly. If its explanation is wrong, correct it before asking for the fix.

Code review and security checks

AI code review is useful for a first pass before a human reviewer reads the pull request. Ask the model to look for common issues: unhandled exceptions, SQL injection through string concatenation, missing authorization checks, synchronous calls in async methods, and IDisposable objects not wrapped in using statements.

For security-sensitive code, treat AI review as a supplement to a human security review, not a replacement. Models miss subtle authorization issues, insecure deserialization patterns, and cryptographic mistakes that require a reviewer who understands the threat model. OWASP's Top 10 is worth reviewing for the specific vulnerability classes that affect .NET web applications.

Test generation

AI generates unit test scaffolding quickly. Give it the method under test, the testing framework you use (xUnit, NUnit, MSTest), and the mocking library (Moq, NSubstitute). Ask for tests covering the happy path, edge cases, and at least one exception path. Review the generated tests for whether they actually test what the method is supposed to do, not just whether it runs.

A common failure mode is AI-generated tests that assert the implementation rather than the behavior. A test that just verifies a method was called is not a useful test if the behavior is more complex. Review tests for the "arrange, act, assert" structure and check that the assert verifies meaningful output.

Lessons that apply to future coding sessions

A .NET developer accumulates knowledge about where AI suggestions go wrong on a specific project: deprecated patterns it keeps suggesting, a library version mismatch it gets wrong, an architecture constraint it ignores. Recording those as lessons means the next session starts with those traps already flagged.

The what-is-cursor-ai article covers one of the popular AI coding tools in more depth. For the broader question of AI agents in solo development, see solo developer AI coding agents.

How to do this with fenbs

1. Create a free account and add a project for the solution or service you are working on.
2. Record the architecture decisions in Decisions and rules: target framework, library choices, patterns in use, and anything the team has agreed not to do.
3. After any session where an AI suggestion introduced a bug or required significant correction, record the lesson: what the model got wrong, why, and what to add to the context to prevent it.
4. Add tasks for the current work. Record the outstanding questions and design decisions that need resolution.
5. Connect an AI app from Integrations. Ask it to read the project context, architecture rules, and lessons before each coding session.