Building a Developer Copilot for Internal Documentation Using Semantic Kernel

Introduction

One of the biggest challenges in software development is finding accurate and up-to-date information. As organizations grow, documentation becomes scattered across multiple platforms such as wikis, knowledge bases, SharePoint sites, Git repositories, architecture documents, and internal portals.

Developers often spend valuable time searching for answers to questions like:

How does this service work?
Which API should I use?
Where is the deployment guide?
What are the coding standards?
How do I troubleshoot a specific issue?

Traditional search systems rely heavily on keywords and frequently fail when developers phrase questions differently than the documentation.

A Developer Copilot solves this problem by using AI to understand natural language questions and retrieve relevant information from internal documentation. By combining Semantic Kernel with modern AI models, developers can build intelligent assistants that provide contextual answers directly within development workflows.

In this article, you'll learn how to build a Developer Copilot for internal documentation using Semantic Kernel and .NET.

What Is a Developer Copilot?

A Developer Copilot is an AI-powered assistant that helps developers access organizational knowledge through conversational interactions.

Instead of manually searching through multiple systems, developers can ask questions such as:

How do I authenticate with the Order API?

Or:

What is the deployment process for the
billing service?

The Copilot retrieves relevant documentation and generates a concise answer.

Benefits include:

Faster knowledge discovery
Reduced onboarding time
Improved productivity
Consistent information access
Better developer experience

Why Use Semantic Kernel?

Semantic Kernel is an open-source framework that simplifies AI application development in .NET.

Key capabilities include:

AI model integration
Prompt orchestration
Memory management
Function calling
Agent workflows
Retrieval-Augmented Generation (RAG)

These features make Semantic Kernel an excellent foundation for building internal developer assistants.

Understanding the Architecture

A typical Developer Copilot consists of:

Documentation Repository
Embedding Generation Service
Vector Database
Semantic Kernel Application
AI Model
Chat Interface

Architecture:

Internal Documentation
          |
          v
Embedding Generator
          |
          v
Vector Database
          |
          v
Semantic Kernel
          |
          v
AI Model
          |
          v
Developer Copilot

This architecture enables semantic search and contextual responses.

Preparing Documentation for Search

The first step is collecting documentation from internal sources.

Common sources include:

Markdown files
SharePoint pages
Wikis
Confluence
Git repositories
Internal portals

Example document:

Order Service Authentication

All requests must include a Bearer token
issued by the Identity Service.

These documents are indexed and stored for retrieval.

Creating a Documentation Model

Define a model to represent indexed content.

public class DocumentationRecord
{
    public string Id { get; set; }
        = string.Empty;

    public string Title { get; set; }
        = string.Empty;

    public string Content { get; set; }
        = string.Empty;
}

This model becomes the foundation for document storage and retrieval.

Configuring Semantic Kernel

Install the required package:

dotnet add package Microsoft.SemanticKernel

Create a kernel instance:

using Microsoft.SemanticKernel;

var builder =
    Kernel.CreateBuilder();

var kernel =
    builder.Build();

The kernel acts as the orchestration layer between AI services and application logic.

Connecting an AI Model

builder.AddAzureOpenAIChatCompletion(
    deploymentName: "gpt-model",
    endpoint: endpoint,
    apiKey: apiKey);

This enables Semantic Kernel to generate responses using organizational knowledge.

Implementing Semantic Search

A key component of a Developer Copilot is semantic search.

Traditional search:

Search:
Order Authentication

Documentation:

Bearer Token Configuration

Keyword search may fail.

Semantic search uses embeddings to identify related meanings rather than exact words.

Workflow:

User Question
      |
      v
Embedding Generation
      |
      v
Vector Search
      |
      v
Relevant Documents

This significantly improves retrieval quality.

Creating a Documentation Retrieval Service

Define a retrieval service interface.

public interface IDocumentSearchService
{
    Task<List<DocumentationRecord>>
        SearchAsync(string query);
}

This service retrieves the most relevant documents for a given question.

Example implementation:

var documents =
    await searchService
        .SearchAsync(userQuestion);

The retrieved content is then provided to the AI model.

Building a Retrieval-Augmented Generation Workflow

Retrieval-Augmented Generation (RAG) combines document retrieval with AI generation.

Workflow:

Question
   |
   v
Vector Search
   |
   v
Relevant Documents
   |
   v
AI Model
   |
   v
Answer

Example question:

How do I authenticate with the Order API?

Retrieved document:

Use a Bearer token issued by the
Identity Service.

Generated answer:

The Order API requires a Bearer token
issued by the Identity Service. Include
the token in the Authorization header.

This produces more accurate and context-aware responses.

Creating a Chat Endpoint

Expose the Copilot through an ASP.NET Core API.

app.MapPost("/copilot",
    async (
        string question,
        ICopilotService service) =>
{
    var response =
        await service
            .AskAsync(question);

    return Results.Ok(response);
});

This endpoint becomes the entry point for developer interactions.

Practical Example

Suppose a new developer asks:

How do I deploy the inventory service?

The system:

Generates embeddings for the question.
Searches indexed documentation.
Retrieves deployment instructions.
Generates a concise answer.

Response:

Deploy the Inventory Service using the
standard deployment pipeline. Ensure the
required environment variables are
configured before deployment.

This eliminates the need for manual document searches.

Improving Responses with Metadata

Metadata can improve retrieval accuracy.

Example:

public string Category
{
    get;
    set;
} = string.Empty;

Possible categories:

Authentication
Deployment
APIs
Infrastructure
Security

Metadata helps narrow search results and improve relevance.

Best Practices

Keep Documentation Current

AI systems are only as effective as the documentation they access.

Regularly update:

API references
Architecture guides
Deployment instructions
Operational procedures

Use High-Quality Embeddings

Embedding quality directly affects retrieval accuracy.

Choose embedding models optimized for semantic search workloads.

Implement Access Controls

Not all documentation should be accessible to every user.

Apply authorization rules before returning documents.

Monitor Search Effectiveness

Track:

Search success rate
User satisfaction
Retrieval accuracy
Frequently asked questions

These metrics help improve the system over time.

Start with a Limited Scope

Begin with a specific documentation domain before expanding to the entire organization.

This simplifies implementation and validation.

Common Challenges

Organizations building Developer Copilots often encounter:

Outdated documentation
Duplicate content
Poor metadata quality
Large document volumes
Access control requirements

Addressing these challenges early improves long-term success.

Conclusion

A Developer Copilot can dramatically improve how teams access and use internal knowledge. Instead of spending time searching through scattered documentation, developers can ask questions in natural language and receive contextual, AI-generated answers.

By combining Semantic Kernel, vector search, and Retrieval-Augmented Generation, organizations can build intelligent documentation assistants that improve productivity, accelerate onboarding, and enhance the overall developer experience. As documentation ecosystems continue to grow, AI-powered knowledge discovery will become an increasingly valuable capability for modern engineering teams.