Making Sense of Old Sitecore Codebases with AI

Introduction

Understanding and modernizing old codebases can be a daunting task, especially when there's no one to guide you. In this post, I'll share my experience using modern AI-powered tools to make sense of complex Sitecore solutions and transform them into maintainable, modern applications, which to me has been a game-changer.

Modern AI IDEs, such as Cursor IDE and Windsurf, use advanced machine learning algorithms to index entire codebases, creating embeddings that represent the semantic structure and relationships within the code. This process allows developers to interact with the codebase as if they were conversing with an expert intimately familiar with the project.

Key Features of Modern AI Development Tools

Codebase Indexing: In my daily work with Sitecore solutions, I've found tools like Cursor IDE invaluable. They compute embeddings for each file in your codebase, which dramatically improves the accuracy of AI responses when dealing with complex Sitecore implementations.
Context-Aware Conversations: I've been particularly impressed with Windsurf's Cascade feature. It dives deep into your codebase, understanding the structure and logic semantically - which is especially helpful when dealing with Sitecore's modular architecture.
Advanced Search Capabilities: When working with large Sitecore solutions that often span multiple projects and repositories, I rely on AI-powered search tools like Windsurf's Riptide. It can quickly index millions of lines of code with high accuracy, making it much easier to navigate complex solutions.

Asking Questions About Your Codebase

In my experience working with various Sitecore projects, these are some of the most effective questions I've used when exploring a new codebase:

"Where is the login functionality implemented in this Sitecore project?"
"Show me all the product listing and details pages."
"What database queries are being used for user authentication?"
"Identify all API endpoints related to order processing."
"Explain the caching mechanism used for content delivery."
"List all custom Sitecore pipelines in this project."
"Find all usage of deprecated Sitecore APIs."

Effective Follow-Up Questions

When asking follow-up questions, I've learned several techniques that work particularly well with AI tools, especially when dealing with Sitecore-specific code:

Maintain Context: I always use keywords like "my Sitecore solution," "my Helix modules," or "my project" to ensure the AI focuses on the specific codebase I'm working with.
Be Specific: I've found it helpful to provide relevant details about the Sitecore version, implementation patterns (like Helix), and specific modules without overloading the AI with unnecessary information.
Use Examples: When asking for code explanations or translations, I provide sample snippets to guide the AI's response.
Chain of Thought Prompting: I ask the AI to explain its reasoning, which can lead to more accurate and insightful answers.
Iterative Refinement: If the initial response isn't satisfactory, I rephrase my question or provide additional context to get better results.

Fixing and Transforming Legacy Code

In my years of working with Sitecore, I've encountered numerous legacy implementations that needed modernization. Here's how I use AI to transform outdated code:

Code Analysis

I start by asking the AI to analyze the existing Sitecore implementation, looking for things like:

Outdated Sitecore API usage
Non-Helix compliant code organization
Performance bottlenecks in content delivery
Security vulnerabilities in custom modules
And more...

Fixing Errors

Often, I can simply paste an error message and ask the AI to suggest a fix. For example:

"I'm getting this error in my Sitecore MVC component: [paste error]. Can you suggest a fix?"

The AI can analyze the error, consider the context of my codebase, and propose a solution. It might take a few tries at resolving the error, especially if it's something runtime, but quite often it does a good job, especially on a simpler tasks or things like style changes.

Transforming to New Tech Stacks

AI IDEs can also help in migrating legacy code to modern frameworks. For instance, to convert old Sitecore MVC components to Next.js components for Sitecore XM Cloud, I might ask:

"Convert this Sitecore MVC component to a Next.js component compatible with Sitecore XM Cloud:

[Paste your Sitecore MVC component code here]"

The AI will analyze the code and provide a transformed version, often with explanations of the changes made.

A few tips for AI-Assisted Code Transformation

Based on my experience of using AI IDEs in my Sitecore projects, here are some key practices I follow:

Take some time to master the aspects of prompt engineering: AI tools are incredibly powerful, but they do what they're asked, so... good prompts lead to good results and bad prompts lead to bad results.
Understand the limitations: AI tools are not perfect, and they can sometimes make mistakes. It's important to understand how they work and how to best use them.
Don't blindly test the AI (yet) - always review and Test: I always thoroughly review AI-generated code and test it, especially when dealing with critical Sitecore components like pipelines and event handlers.
Understand the Changes: I ask the AI to explain the transformations it suggests, particularly when dealing with Sitecore-specific patterns and best practices.
Incremental Updates: When modernizing large Sitecore solutions, I prefer making incremental changes to minimize risks and ensure stability.
Documentation: I make sure to document all transformations, especially those involving Sitecore configuration changes or architectural updates.
Combine AI with Human Expertise: I use AI as a tool to augment my development process, not replace human oversight and decision-making.

Conclusion

Through my journey of working with various Sitecore implementations, I've found that modern AI tools have revolutionized how we approach legacy code. Whether you're dealing with an outdated Sitecore 8.x solution and/or rewriting an old soluton into the latest tech stack, these tools provide invaluable assistance in understanding and modernizing complex codebases.