What's the most cost-effective way to speed up boilerplate generation using a pair programming assistant?

The most cost-effective way is usually to use the assistant for high-volume, repetitive scaffolding and verification, while keeping your own prompts and templates extremely tight.

Best approach

1. Create reusable prompt templates

   • For common tasks like:
     • API endpoint scaffolding
     • CRUD components
     • test generation
     • DTO/model boilerplate
   • Save them as snippets so you’re not re-explaining context every time.

2. Feed the assistant small, structured inputs

   • Instead of dumping a whole codebase, give:
     • language/framework
     • file name
     • desired pattern
     • one or two examples
   • This reduces token usage and iteration time.

3. Use it to generate “first drafts,” not final code

   • Ask for:
     • class/function skeletons
     • repetitive getters/setters
     • tests based on existing patterns
     • documentation stubs
   • Then review and refine yourself.

4. Leverage repo context only when needed

   • If the assistant has access to your codebase, use it selectively.
   • Constantly loading large contexts is convenient but often less cost-effective.

5. Standardize patterns

   • The more your project uses consistent conventions, the easier it is for the assistant to generate boilerplate correctly on the first pass.
   • This gives the biggest ROI.

Most economical workflow

• You define the pattern once
• Assistant generates repetitive files
• You do a quick review and patch
• Save the prompt for reuse

Practical rule of thumb

If a task is:

• repetitive
• low-risk
• pattern-based

then it’s ideal for pair programming assistant usage.

If you want, I can also suggest:

• the cheapest tool setup for this,
• a prompt template, or
• a workflow for a specific language/framework.