Supercharging AI Coding Assistants with Gemini Models' Context
One of the most exciting frontiers in the application of long-context windows is code generation and understanding. Large codebases require a deep understanding of complex relationships and dependencies, something traditional AI models struggle to grasp. By expanding the amount of code with large context windows, we can unlock a new level of accuracy and usefulness in code generation and understanding.
We partnered with Sourcegraph, the creators of the Cody AI coding assistant that supports LLMs like Gemini 1.5 Pro and Flash, to explore the potential of long context windows in real-world coding scenarios. Sourcegraph's focus on integrating code search and intelligence into AI code generation, and successful deployment of Cody to enterprises with large, complex codebases such as Palo Alto Networks and Leidos, made them the ideal partner for this exploration.
Sourcegraph compared Cody's performance with a 1M token context window (using Google's Gemini 1.5 Flash) against its production version. This direct comparison allowed them to isolate the benefits of expanded context. They focused on technical question answering, a crucial task for developers working with large codebases. They used a dataset of challenging questions that required deep code understanding.
