SvelteKit Refactor with AI: Advanced Strategies for Idiomatic Code

Many developers are exploring how AI can streamline programming tasks, especially for large refactoring efforts like translating a legacy jQuery and Django project into a modern SvelteKit application. While the promise of AI-assisted coding is high, achieving desired code quality and efficiency can be challenging. This article distills practical strategies and insights shared by experienced AI users to help bridge the gap between AI's potential and its effective application in real-world development.

1. Master Context Management and "Memory"

A cornerstone of successful AI interaction is managing the conversational context. Models like Claude often read special files, such as CLAUDE.md, to receive project-specific guidelines, coding conventions, and instructions on common errors to avoid. However, LLMs can exhibit a "recency bias" or "context rot," sometimes appearing to forget earlier instructions in longer conversations.

To mitigate this:

• Frequent Context Resets: Start new conversations for distinct tasks. For longer projects, break down work into smaller, self-contained conversations.
• Concise Guidelines: Keep your CLAUDE.md file focused, ideally under 1,000 tokens. Regularly prune outdated information.
• Explicit Injection: Some tools allow explicit file imports (e.g., @AGENTS.md) to ensure critical context is always present.
• AI-Assisted Reflection: You can prompt the AI to reflect on a successful interaction and condense the learned lessons into a new entry for your guideline file.
• Leverage Skills and Slash Commands: For repetitive workflows, consider building custom "skills" or "slash commands" that encapsulate complex tool interactions or rule sets, reducing the need for verbose, repeated prompts.

2. Embrace Structured Planning

One of the most impactful tips is to treat the AI as a collaborative planning partner. Dedicated "Plan modes" (e.g., in Claude Code) allow you to iterate on a detailed implementation strategy before any code is written.

• Iterative Planning: Go back and forth with the AI to refine the plan until it aligns with your architectural vision.
• Document the Plan: Have the AI generate a detailed plan document (e.g., plan.md) that outlines steps, component boundaries, and logic. This document can serve as a shared reference and can be edited manually.
• Break Down Tasks: Divide large refactoring goals into smaller, verifiable chunks. Each chunk should be manageable within a single AI interaction or a short series of steps.
• High-Level Artifacts: For complex projects, generate intermediate artifacts like architecture descriptions or high-level design documents before moving to code.

3. Implement Robust Feedback Loops

AI-generated code, especially early on, might not be perfect. The key to improving its quality is to provide mechanisms for the AI to "check its own work."

• Test-Driven Development (TDD): This approach is highly recommended. Ask the AI to write tests first, then implement the code to pass those tests. The tests act as executable specifications and guardrails.
• Browser-Based Verification: For UI tasks (like SvelteKit conversions), integrate tools like Puppeteer or Playwright. The AI can then launch a browser, interact with the UI, and verify visual or functional correctness.
• Automated Code Quality Checks: Set up linters, formatters, and custom pre-build scripts to enforce coding standards, architectural rules, and identify common pitfalls. The AI can be instructed to pass these checks before presenting code.
• Log Analysis: For debugging, have the AI generate and analyze logs to pinpoint issues and suggest fixes.

4. Optimize Prompting for Precision and Clarity

The way you communicate with the AI significantly impacts the quality of its output.

• Be Specific and Concise: Define "idiomatic" code with clear examples of good and bad patterns. Avoid vague terms.
• Voice Transcription: Leverage voice-to-text tools to quickly articulate complex thoughts and detailed instructions without being constrained by typing speed. The AI can often interpret winding verbal prompts surprisingly well.
• Show, Don't Just Tell: Provide small, finished code examples from your project and instruct the AI to match that level of abstraction and composition.
• Use Constraints: Explicitly forbid unwanted patterns (e.g., "Do not use boolean flags for view switching; use composable components"). Forbidding often yields better results than simply asking for an ideal solution.
• Iterate and Refine: Treat the interaction as a conversation. Ask the AI to clarify its understanding or suggest alternatives. Be prepared to cancel and redirect if the AI goes off track.

5. Choose the Right Model and Tooling

The rapidly evolving AI landscape means model choice matters.

• Advanced Models: Models like Claude Opus 4.5 are frequently cited for superior code quality and adherence to instructions, often proving more cost-effective in the long run due to greater efficiency despite higher per-token prices.
• Experiment with Tools: Explore agentic coding environments like Claude Code, Cursor, Google Antigravity, or Aider. Each offers different UI/UX and model integrations.
• Language-Specific Support: Be aware that models might perform differently across languages or frameworks. Svelte, being newer, may have less training data, sometimes leading to less idiomatic code compared to more established frameworks like React.

6. Understand AI's Role and Limitations

Finally, adjust expectations and integrate AI thoughtfully.

• Thinking Partner, Not Replacement: View the AI as an intelligent assistant or a "rubber duck" to bounce ideas off, generate boilerplate, or automate tedious tasks. It's a tool to amplify your skills, not to replace your critical thinking or understanding of the codebase.
• Quality vs. Speed: For high-stakes production code, human review remains paramount. While AI boosts velocity, it’s crucial to ensure that early speed gains don't accumulate technical debt later.
• Start Small: Begin with small, manageable tasks where you can easily verify the output. Gradually scale up as you build experience and confidence in your AI workflow.

By adopting these disciplined approaches, developers can significantly enhance their efficiency and code quality when using AI for programming, transforming challenging refactoring projects into more manageable and accelerated endeavors.