2x Faster, 30x Cheaper Prompt Enhancement in Kilo Code
Get better prompts faster!
Optimizing your workflow is essential for productive coding sessions. Today we're exploring a simple yet powerful configuration tweak for Kilo Code that can significantly speed up your experience while reducing costs.
Why Good Prompts Matter
In AI-assisted coding, the quality of your prompt directly impacts the quality of the code you receive. A well-crafted prompt can:
Save you time by getting the right solution on the first attempt
Reduce the need for follow-up corrections and clarifications
Ensure consistent formatting and coding standards
Help the AI understand complex requirements accurately
Make the difference between usable code and code that needs significant reworking
Save both money and time by minimizing token usage on rework and iterations
That last point is particularly important: every time you need to clarify, correct, or refine the AI's output, you're spending additional tokens (money) and time. Good prompts dramatically reduce these costly iterations, making your development process more efficient and economical.
The Enhance Prompt feature
The Enhance Prompt feature helps bridge the gap between what you initially type and what the AI needs to understand your intent fully. It's an essential productivity tool for developers working with AI coding assistants.
While this feature is incredibly useful, waiting for prompt enhancement can sometimes slow down your workflow, especially when using larger, more sophisticated models like Claude 3.7 Sonnet.
What if you could make the enhance prompt feature twice as fast while maintaining quality? By configuring different AI models for different tasks, you can optimize both speed and cost:
Keep powerful models like Claude 3.7 Sonnet for complex coding tasks
Use faster, more economical models like GPT-4.1-Nano for prompt enhancement
In our tests, this configuration reduced prompt enhancement time from 9 seconds to just 4 seconds—more than 2X faster! Additionally, it dramatically reduced costs, as GPT-4.1-Nano is substantially more economical than Claude 3.7 Sonnet.
How to Configure Different Models for Different Tasks
Setting up this optimization is straightforward. Let's walk through the process step by step:
Step 1: Create a New Configuration Profile
Go to Settings → Providers
Create a new configuration profile (e.g., "Nano")
Select the same API provider you normally use
Instead of using Claude 3.7 Sonnet, choose GPT-4.1-Nano (or any other lightweight model of your choice)
NOTICE You also can use different profiles for different modes!
Step 2: Configure the Enhance Prompt Feature
Navigate to the Prompts tab
Scroll down to the "Support Prompts" tab
Under API Configuration for prompt enhancement, change from "currently selected" to your new "Nano" profile
Save your changes
Now when you use the Enhance Prompt feature, Kilo Code will utilize the faster, more economical GPT-4.1-Nano model instead of your main model, while still using your preferred powerful model for the actual coding tasks.
The Benefits at a Glance
This simple configuration change delivers significant advantages:
Speed: Prompt Enhancement is more than twice as fast (4s vs 9s for a simple prompt), creating a smoother workflow with less waiting.
Cost Efficiency: GPT-4.1-Nano costs just $0.10 per million input tokens and $0.40 per million output tokens, compared to Claude 3.7 Sonnet's $3.00 and $15.00 per million tokens. That's up to 97% cost savings!
Same Quality: Despite being faster and cheaper, GPT-4.1-Nano provides high-quality prompt enhancements that are just as effective for this specific task.
Real-World Application
This optimization is particularly valuable when you find yourself using the Enhance Prompt feature frequently. For example, when:
Working with complex documentation that requires precise prompting
Developing in unfamiliar frameworks where prompt clarity is essential
Collaborating with team members who rely on well-structured prompts
Working through iterative development sessions with multiple prompt enhancements
In our testing, we've found this technique works consistently across various types of prompt enhancement scenarios without any degradation in the quality of enhanced prompts.
Final Thoughts
This model-switching approach represents one of the most practical optimizations you can make to your Kilo Code workflow. It demonstrates how thoughtful configuration of your AI tools can lead to significant performance improvements and cost savings.
The beauty of this technique lies in its simplicity—just a few configuration changes can dramatically enhance your experience without compromising the powerful AI capabilities you rely on for your coding tasks.
For a complete visual walkthrough, don't forget to check out our step-by-step video tutorial.
How do you optimize your Kilo Code workflow? Have you found other ways to improve performance or reduce costs? Drop your ideas in the comments below or join our discord server!