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Efficient Code Search with Nvidia DGX
Large language models (LLMs) have enabled AI tools that help you write more code faster, but as we ask these tools to take on more and more complex tasks, there are limitations that become apparent.
This specialized embedding model can significantly enhance the performance of RAG systems in software development contexts, helping to improve code completion, bug detection, and the generation of technical documentation. To achieve this goal, we substituted existing industry-standard components in the Genie project pipeline with Qodo’s specialized alternatives, improving the system’s ability to mine NVIDIA’s internal code repositories and yielding superior results. The final pipeline was integrated into NVIDIA’s internal Slack system, allowing expert C++ developers to ask detailed technical questions based on repositories of interest and receive robust responses.
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