Building a personalized code assistant with open-source LLMs using RAG Fine-tuning

RAG fine-tuning has shown significant improvements in code generation accuracy, offering 3.7x faster speed and a cost reduction of up to 150x compared to existing models like Claude 3 Opus and GPT-4o. By leveraging the Together API and Morph Labs' advanced technologies in codebase search and synthetic data generation, this approach enables personalized code assistants with repository-level context and fine-tuning an open-source LLM, making these models more practical and valuable tools for developers. The technique addresses the limitations of outdated knowledge and hallucinations in LLMs, achieving up to 19% quality improvement, 1.1x faster speed at 37.5x cost reduction compared to GPT-4o, while offering 16% better accuracy than Claude 3 Opus.

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