This research explores how Retrieval-Augmented Generation (RAG) can enhance large language models' ability to generate code using unfamiliar API libraries.

• RAG significantly improves LLMs' performance when working with less common or fast-evolving APIs
• The approach mimics how human developers consult documentation when coding
• Results show measurable gains in code quality and accuracy compared to non-augmented models
• Particularly valuable for enterprise environments with specialized or proprietary APIs

For engineering teams, this research offers a practical path to extend LLMs' capabilities beyond their pre-trained knowledge, enabling more effective developer assistance tools and reducing the learning curve for new APIs.

Original Paper: When LLMs Meet API Documentation: Can Retrieval Augmentation Aid Code Generation Just as It Helps Developers?