Refactoring Code



Models

  • o3-mini
  • o1-mini
  • gpt-4o-mini
  • gpt-4o
  • chatgpt-4o-latest



Breakdown

Refactoring breakdown example of prompt.ts

Below is one way to describe what refactoring might look like for each "model" (here understood as a refactoring‐strategy level) along with the corresponding "cost" in OpenAI tokens. In our example, the dictionary

{ "o3-mini": 0, "o1-mini": 1, "gpt-4o-mini": 2, "gpt-4o": 3, "chatgpt-4o-latest": 4 }

indicates that the simplest "refactoring model" (o3-mini) would propose almost no changes (0 tokens) while the most "expensive" one (chatgpt-4o-latest) would outline a comprehensive refactor (costing 4 tokens).

Below are sample refactoring approaches for each "model":



1. o3-mini (Token Cost: 0)

  • Minimal changes only—essentially "no‐refactor."
    – Leave the code structure mostly intact.
    – Perhaps perform very light cleanup (for example, consolidating redundant import statements or fixing minor spacing/formatting issues).
  • Outcome: Code remains almost as originally written with nearly zero additional processing cost.



2. o1-mini (Token Cost: 1)

  • Light refactoring with one or two minor improvements.
    – Extract the small utility (for example, the extractErrorMessage method) so that error handling is slightly centralized.
    – Simplify minor code repetitiveness (such as unifying similar error messages).
  • Outcome: A few methods moved to a helper file with a small cost (1 token).



3. gpt-4o-mini (Token Cost: 2)

  • Moderate refactoring with a couple of clear improvements.
    – Separate concerns by moving API interaction logic (submitQuestion and pollForResponse) into its own service module.
    – Clean up the command’s control flow (for example, isolate file reading and prompt gathering into separate helper functions).
  • Outcome: The source code is split into distinct modules (for config, API calls, and CLI handling) and is easier to maintain.
  • Token cost: Around 2 tokens for the improvements.



4. gpt-4o (Token Cost: 3)

  • More extensive refactoring with several structural changes.
    – Modularize the code: create separate modules for configuration (loadConfig), API handling (HTTP calls via axios), and the CLI interface (ask command).
    – Improve error handling (for example, centralize error logging or add custom error classes).
    – Introduce better type definitions and possibly separate business logic from user interaction.
  • Outcome: The code is reorganized into cohesive units that are more testable and maintainable, at a cost estimated around 3 tokens.



5. chatgpt-4o-latest (Token Cost: 4)

  • Full-blown comprehensive refactor that touches almost every aspect of the module.
    – Refactor the command into a thin controller layer while offloading API interactions, file input handling, and error management into separate, well-documented service modules.
    – Adopt a consistent coding style (e.g. uniform quotation marks, standardized async/await error handling) across the entire file.
    – Introduce a robust logging mechanism and possibly integrate automated testing for the refactored modules.
    – Optionally rework the polling logic to use more sophisticated patterns (such as an event-driven design or reactive streams) if needed.
  • Outcome: A thoroughly modernized codebase that supports maintainability, scalability, and easier debugging, with the improvements "costing" about 4 tokens in estimated OpenAI usage.



Summary

Each "model" in our list represents a different "level" of refactoring intensity, with the number indicating the estimated token count (or "cost") to generate that full refactor plan via an API call. Lower token costs (0–1) mean very little change, while higher costs (3–4) mean a comprehensive, multi-step transformation. The above breakdown serves as one interpretation of how you might refactor the provided prompt.ts file in progressively larger "chunks" of change.