Scaling Code Understanding: How Jolt AI Leverages the Gemini API

Developers working with sprawling, production-scale codebases know the pain. Understanding context, finding relevant files, and making changes can feel like navigating a labyrinth. Jolt AI is tackling this head-on with a codegen and chat tool designed specifically for real-world, 100K+ line codebases. Their secret weapon for delivering both speed and accuracy? The Gemini API, particularly Gemini 2.0 Flash.

Jolt AI's mission is to enable developers to instantly understand and contribute to any codebase. Many of today's tools struggle with large, existing codebases and require users to manually select context files. It's tedious and impractical. Jolt AI uses a novel semantic search that accurately and automatically identifies the relevant context files. It's a game-changer for feature development, bug fixing, onboarding, and more.

The challenge for Jolt AI was finding a model that could power their search pipeline with the right blend of speed, consistency, and code understanding. "We were looking to speed up 3 AI-backed steps in our code search pipeline," explains Yev Spektor, CEO of Jolt AI. "Each step requires an understanding of various programming languages, frameworks, user code, and user intent."

Enter Gemini 2.0 Flash. For Jolt AI, this model delivered the performance leap they were seeking. "After some prompt tuning, we were able to get more consistent, higher-quality output with Gemini 2.0 Flash than we had with a slower, larger model from another provider," Spektor notes.

How is Jolt AI using Gemini 2.0 Flash? It powers several crucial steps in their code search pipeline, providing the speed and accuracy needed to navigate and understand massive repositories. While the exact details are their "secret sauce," the impact is clear: Gemini 2.0 Flash enables Jolt AI to quickly surface the right information within complex codebases.

Switching to the Gemini API was remarkably efficient. "A couple hours to get the SDK implemented, and 2 days for prompt tuning and testing," reports Spektor. The team also utilized Google AI Studio for prompt ideation and tuning, streamlining the development process.

The move to Gemini 2.0 Flash has yielded impressive results for Jolt AI:

• 70-80% Reduction in response times: The AI-backed steps in their search pipeline are significantly faster.
• Higher quality and more consistent answers: Users receive better results more than twice as fast.
• 80% Lower costs: The migrated AI workloads are now significantly more cost-effective.

"We are getting higher-quality answers to our users more than twice as quickly," Spektor emphasizes. This combination of speed, quality, and cost savings underscores the power of Gemini 2.0 Flash for performance-critical applications.

Jolt AI is actively expanding its IDE support with an upcoming JetBrains plugin and exploring API accessibility. Spektor is excited about the broader potential of Jolt AI across enterprises, from aiding developers and engineering leaders to supporting customer support teams and enabling automated AI code pipelines.

Reflecting on their journey with the Gemini API, Spektor offers this advice to fellow developers:

"Gemini 2.0 Flash is more capable than you think, don’t sleep on it. It’s very good at recall - much better than some slow, more expensive models." He also encourages developers to explore the latest models from the Gemini family: "The new generation, Gemini 2.0 Flash and Gemini 2.5 Pro, need to be looked at. Gemini 2.0 Flash has made our product over twice as fast while increasing the quality of responses. The new models are a major step function."

Jolt AI's success story highlights how the speed and capability of Gemini 2.0 Flash can significantly enhance AI-powered developer tools, especially those dealing with the complexities of large codebases.

Ready to build? Explore the Gemini API documentation and get started with Google AI Studio today.