Article : Teaching AI Your Codebase: Strategies for Contextual Prompting

AI tools like ChatGPT and GitHub Copilot are revolutionizing software development, offering developers unprecedented assistance. However, their effectiveness often depends on the context provided. Without a comprehensive understanding of your project, these tools can generate code that’s functional but misaligned with your architecture, naming conventions, or overall goals.

In this article, we’ll explore strategies to teach AI about your codebase, enabling more relevant and accurate suggestions.

1. Why Context Matters

AI models generate output based on the information in the prompt. Without sufficient context, they can make uninformed decisions, such as:

• Using incorrect variable names.
• Generating duplicate or redundant functions.
• Ignoring existing architectural patterns or conventions.

By feeding AI tools with the right information, developers can overcome these challenges and align the AI-generated output with the project’s specific needs.

2. Strategies for Providing Context

2.1 Share Relevant Code Snippets

When asking the AI to modify or extend existing functionality, include the specific code snippet it needs to work with.

• Example Prompt:
  “Here is my User model and related repository pattern. Can you write a function to fetch users created in the last 30 days?”

• class User:
      def __init__(self, id, name, created_at):
          self.id = id
          self.name = name
          self.created_at = created_at
  
  class UserRepository:
      def __init__(self):
          self.users = []
  
      def add_user(self, user):
          self.users.append(user)

2.2 Define Project Conventions

Clearly explain the naming, style, or organizational conventions of your project.

• Example Prompt:
  “All functions in this project use snake_case naming, and variables are prefixed with the module name.”

2.3 Specify Dependencies

Inform the AI about key libraries or frameworks you’re using.

• Example Prompt:
  “This project uses Django ORM for database interactions. Generate a query to retrieve users who haven’t logged in for 90 days.”

3. Incorporating Documentation and ReadMe Files

Uploading or referencing project documentation helps the AI understand the broader scope.

• Include high-level overviews from the README, such as:
  • Project purpose.
  • Core modules and their responsibilities.
  • Key design principles.
• Provide a glossary of terms or abbreviations specific to your domain to avoid confusion.

Example: Summarizing a README for Context

“Here’s the project overview:
This is a web application for tracking personal fitness goals.

• Backend: Django REST Framework.
• Frontend: React.
• Database: PostgreSQL.
  Can you write a Django view for creating a new fitness goal?”

4. Segment and Focus Large Codebases

For extensive codebases, focus the AI’s attention on relevant sections.

• Use prompts like:
  • “This is the code for the authentication module. Can you extend it to support two-factor authentication?”
  • “The following functions handle file uploads. Add a feature for file type validation.”

Organize by Module:

Rather than dumping large chunks of unrelated code, narrow down the context by module or functionality.

5. Creating a Persistent Knowledge Base for AI

Some AI tools support fine-tuning or custom embeddings, allowing developers to “teach” the model about their codebase.

5.1 Training Custom Models

• Use training data to teach the model project-specific conventions.
• Fine-tune using past code examples and their explanations to align the AI’s output with your style.

5.2 Leveraging Contextual Memory

Some tools allow for multi-turn conversations, where context from earlier exchanges is retained. Structure interactions to build upon previous queries:

1. Share the main architecture.
2. Introduce a module.
3. Request specific improvements or additions.

6. Practical Tips for Contextual Prompting

6.1 Be Explicit

Avoid ambiguous instructions. Specify every detail about your needs, such as variable names, function purposes, and dependencies.

6.2 Keep It Concise

While more detail is helpful, overly long prompts can lead to errors or irrelevant suggestions. Provide only what’s necessary for the task.

6.3 Test and Iterate

Experiment with different levels of detail in your prompts to find the balance that works best for your project.

6.4 Reference Outputs

Ask the AI to analyze or improve its own output:

• “This is the function you generated. Does it follow best practices for scalability and maintainability?”

7. Limitations to Consider

• Memory Constraints: Most AI tools can only process a limited amount of context at a time. Break down requests into manageable chunks.
• Lack of Holistic Understanding: Without fine-tuning, the AI may still fail to grasp complex interdependencies within your codebase.
• Security Risks: Avoid sharing sensitive or proprietary code in public AI tools. Use local or enterprise-grade AI solutions when handling confidential projects.

8. Future of Contextual AI in Development

As AI tools evolve, we may see features that make teaching them easier, including:

• Dynamic Project Embedding: Upload entire projects for real-time, context-aware assistance.
• Enhanced Context Limits: Models with larger processing windows to handle complex codebases in one go.
• Intelligent Suggestions: AI tools that automatically infer conventions and patterns from a codebase without explicit prompting.

Conclusion

Teaching AI about your codebase through effective contextual prompting transforms it from a generic coding assistant to a powerful collaborator.

By sharing relevant snippets, defining conventions, and leveraging documentation, developers can guide AI tools toward producing code that’s not only functional but deeply integrated into their projects. With practice and the right strategies, these tools can save time and spark creativity without compromising quality or alignment.