AI coding tools are changing how developers work with documentation, APIs, code examples, and project conventions. A coding assistant can generate useful code, but the quality of that code depends heavily on the information available to it.
This becomes a practical problem for development teams.
An internal API changes. A coding standard is updated. A library moves to a new version. A configuration rule changes. If the documentation used by an AI coding tool is not updated, developers may receive suggestions based on outdated information.
One way to solve this problem is to package the documentation with NuGet and deliver updates through the same dependency management workflow already used by .NET projects.
Instead of manually copying documentation into every repository, teams can maintain a versioned documentation package and update it when the underlying API, library, or development guidance changes.
This article explains how this approach works, how to structure the package, how developers can consume it, and what to consider when maintaining documentation for AI-assisted development.
Why AI Coding Tools Need Current Documentation
AI coding assistants can work with project files, source code, configuration, and documentation depending on the tool and its capabilities.
The problem is that documentation becomes stale.
Consider an internal library:
Company.Payments
Version 1.0
|
+-- CreatePayment()
+-- RefundPayment()
Later, version 2.0 changes the API:
Company.Payments
Version 2.0
|
+-- CreatePaymentAsync()
+-- RefundPaymentAsync()
+-- PaymentOptions
If the AI tool still has documentation describing version 1.0, it may suggest code that no longer matches the current library.
The problem is not necessarily the AI model.
The problem is the context provided to it.
Keeping documentation versioned and distributed with the application can reduce this mismatch.
What Does a Documentation NuGet Package Look Like?
A NuGet package does not have to contain executable .NET assemblies.
NuGet can also distribute files such as:
- Markdown documentation
- JSON metadata
- Code examples
- Configuration templates
- API usage guidelines
- Prompt context
- Development rules
- Architecture notes
- Troubleshooting guides
A simplified package could look like this:
company-ai-docs/
README.md
docs/
api.md
authentication.md
examples.md
troubleshooting.md
ai/
coding-guidelines.md
api-context.md
project-rules.md
The package can then be versioned like any other dependency.
For example:
Company.AI.Documentation 1.0.0
Company.AI.Documentation 1.1.0
Company.AI.Documentation 2.0.0
This gives development teams a controlled way to distribute documentation updates.
Why Use NuGet for Documentation?
Most .NET teams already have NuGet-based dependency workflows.
Developers understand how to:
dotnet add package
restore dependencies:
dotnet restore
and update package versions through project files.
Using the same infrastructure for documentation has several advantages.
Versioning
Documentation can be tied to a specific package version.
For example:
<PackageReference Include="Company.AI.Documentation"
Version="2.1.0" />
The project now has an explicit documentation version.
Repeatable Builds
A project can restore the same documentation version in different environments.
Centralized Maintenance
The documentation can be maintained in one repository instead of being copied across multiple application repositories.
Controlled Updates
Teams can choose when to move from version 2.1.0 to 2.2.0 rather than receiving undocumented changes.
Design the Documentation Around Developer Tasks
Do not build a documentation package simply by copying an existing website into a NuGet package.
The package should contain information that helps developers and AI coding tools solve common development tasks.
For example:
docs/
getting-started.md
authentication.md
error-handling.md
pagination.md
caching.md
testing.md
Each document should answer practical questions.
Instead of:
Authentication API
provide useful information such as:
How to authenticate
Required headers
Token lifetime
Common authentication errors
Working code example
Incorrect usage
AI tools benefit from clear examples and constraints because these provide usable context rather than only descriptive text.
Example Documentation File
Suppose a company maintains an internal customer API.
A documentation file could contain:
# Customer API
Use the CustomerClient to retrieve customer information.
## Get Customer
```csharp
var customer = await client.GetCustomerAsync(customerId);
The API requires an authenticated request.
Do not use the deprecated GetCustomer method.
The CustomerId must be a GUID.
Common Errors
404:
The customer does not exist.
401:
The access token is missing or expired.
The important part is that the documentation describes both the correct implementation and constraints.
This gives developers and AI tools useful project context.
## Organize Documentation for Different AI Use Cases
Not every document needs to be exposed to every coding workflow.
A useful structure is:
```text
ai/
coding-guidelines.md
architecture-rules.md
api-patterns.md
common-errors.md
security-rules.md
Then maintain general developer documentation separately:
docs/
getting-started.md
api-reference.md
troubleshooting.md
This separation makes the package easier to consume.
For example, a coding assistant may need:
coding-guidelines.md
api-patterns.md
security-rules.md
while a developer reading the package may need the complete documentation set.
Add Documentation as Package Content
A NuGet package can include files that are not compiled assemblies.
For example, a .csproj file can define package content:
<ItemGroup>
<None Include="docs\**\*"
Pack="true"
PackagePath="contentFiles\any\any\docs" />
<None Include="ai\**\*"
Pack="true"
PackagePath="contentFiles\any\any\ai" />
</ItemGroup>
The exact packaging approach should depend on how the documentation will be consumed.
The important concept is that the package contains versioned documentation alongside the application's dependency metadata.
Keep Documentation Versioned
Documentation should follow the same discipline as source code.
For example:
1.0.0
Initial documentation
1.1.0
Added authentication examples
1.2.0
Updated pagination guidance
2.0.0
Updated API examples for the new client library
Semantic versioning can help communicate the scope of documentation changes.
A documentation-only change may not require the same versioning rules as a runtime API package, but consistent versioning still matters.
Connect Documentation Versions to API Versions
A common mistake is to maintain documentation versions independently from the software they describe.
Suppose:
Company.Api 3.0
Company.AI.Documentation 1.0
If the documentation describes API version 2.0, the AI coding tool can still produce incorrect examples.
A better approach is to clearly identify the supported API version.
For example:
# Customer API Documentation
Supported API version: 3.x
This documentation is intended for:
Company.Api >= 3.0
The documentation package can also use matching release notes.
Company.Api 3.1
|
+-- Company.AI.Documentation 3.1
This is not mandatory, but it can make compatibility easier to understand.
Add Code Examples
Examples are particularly useful for AI-assisted development.
Instead of documenting only the method signature:
Task<Customer> GetCustomerAsync(Guid id);
include a realistic usage example:
var customer = await customerClient
.GetCustomerAsync(customerId);
Console.WriteLine(customer.Name);
Also include incorrect patterns when they are common sources of errors.
For example:
Do not call the synchronous method from request-handling code.
Prefer:
GetCustomerAsync()
instead of:
GetCustomer()
This gives developers a clear implementation rule.
Include Constraints and Rules
Documentation for AI-assisted development should not contain only API descriptions.
It should also define project rules.
For example:
# API Rules
- Use async methods for database and HTTP operations.
- Do not log access tokens.
- Use cancellation tokens for long-running requests.
- Do not create HttpClient instances for every request.
- Use the shared authentication service.
These rules can help keep generated code aligned with existing engineering practices.
Update the Documentation Package Through CI/CD
Documentation should not depend on someone manually creating a package every time an API changes.
A CI/CD pipeline can:
- Build the documentation.
- Validate Markdown files.
- Run documentation tests.
- Package the content.
- Assign a version.
- Publish the NuGet package.
- Make the new version available to consuming projects.
A simplified workflow might look like:
Documentation repository
|
v
Validation
|
v
NuGet package
|
v
Internal NuGet feed
|
v
Application projects
This turns documentation into a maintained engineering artifact instead of a collection of files that developers update manually.
Use an Internal NuGet Feed
For company-specific documentation, publishing to a public NuGet feed may not be appropriate.
An internal package source can keep documentation restricted to authorized development teams.
The project can then reference the internal package:
<ItemGroup>
<PackageReference Include="Company.AI.Documentation"
Version="2.1.0" />
</ItemGroup>
The exact package source depends on the organization's existing artifact-management infrastructure.
The important part is that access control and package governance should be treated as part of the design.
Avoid Putting Secrets in Documentation Packages
A documentation package may be consumed by many developers and automated tools.
Never include:
- API keys
- Passwords
- Access tokens
- Connection strings containing credentials
- Private certificates
- Production secrets
Instead, document the expected configuration.
For example:
Set the API endpoint using the CustomerApi__BaseUrl configuration value.
Do not commit credentials to source control.
Documentation should explain how to configure secrets, not distribute the secrets themselves.
Validate Documentation Before Publishing
A documentation package should have quality checks.
At minimum, validate:
- Markdown syntax
- Broken internal links
- Code examples
- API version references
- Deprecated APIs
- Package version compatibility
- Required documentation files
For code examples, consider compiling or testing important samples.
A documentation update that contains an invalid code example can be just as problematic as an incorrect software release.
Common Problems
Documentation package contains outdated examples
This usually happens when the API changes but the documentation pipeline does not run as part of the release process.
Connect documentation updates to the same release workflow where practical.
Developers have different documentation versions
If one team uses version 1.5 and another uses version 2.1, AI-generated suggestions can differ between projects.
Make the documentation version explicit in the project dependency.
Documentation is too large
A package containing an entire documentation website may become difficult to use.
Keep AI-focused content concise and task-oriented.
Separate detailed reference material from high-value coding guidance.
Documentation describes the API but not how to use it
Method signatures alone are often insufficient.
Include realistic examples, constraints, common mistakes, and recommended patterns.
Package updates happen too frequently
Documentation does not need a new version for every typo.
Establish a practical release policy so developers are not constantly updating dependencies for insignificant changes.
Best Practices
- Treat AI documentation as a versioned engineering artifact.
- Keep documentation close to the code and API lifecycle.
- Use NuGet to distribute versioned documentation to .NET projects.
- Separate general documentation from AI-specific guidance.
- Include realistic code examples.
- Document constraints and common mistakes.
- Tie documentation versions to supported API versions where appropriate.
- Automate package creation through CI/CD.
- Use an internal package feed for private documentation.
- Never place secrets inside documentation packages.
- Validate examples before publishing.
- Keep documentation focused on tasks developers actually perform.
Advantages and Disadvantages
| Area | Advantages | Considerations |
|---|
| Versioning | Documentation has explicit versions | Versions must be maintained |
| Distribution | Developers can consume it through NuGet | Package updates need coordination |
| Consistency | Teams can use the same guidance | Different projects may still pin different versions |
| Automation | CI/CD can publish updates automatically | Pipeline setup requires initial effort |
| AI assistance | Current examples and rules provide better context | Documentation quality still determines usefulness |
| Security | Private feeds can restrict access | Feed permissions must be configured correctly |
| Maintenance | Centralized source reduces duplication | Changes need ownership |
A Simple Documentation Release Workflow
A practical workflow can be kept simple:
1. Update documentation
↓
2. Review code examples
↓
3. Validate links and formatting
↓
4. Update supported API/version information
↓
5. Build NuGet package
↓
6. Run validation
↓
7. Publish package
↓
8. Update consuming applications
For larger organizations, this process can be integrated directly into the API or library release pipeline.
Conclusion
Keeping documentation current is becoming more important as developers use AI coding tools to generate and modify application code.
A versioned NuGet documentation package provides a practical way for .NET teams to distribute coding guidance, API examples, architecture rules, and project-specific instructions alongside the applications that need them.
The main benefit is not simply putting Markdown files inside a NuGet package. The real value comes from treating documentation as a versioned dependency that follows the same release and maintenance process as the software it describes.
When documentation is accurate, versioned, validated, and delivered consistently, developers have a better chance of getting AI-assisted code that matches the current APIs, project conventions, and engineering practices.
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