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AI-assisted Coding with Cody: Lessons from Context Retrieval and Evaluation for Code Recommendations
August 09, 2024 Β· Declared Dead Β· π ACM Conference on Recommender Systems
"No code URL or promise found in abstract"
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Authors
Jan Hartman, Rishabh Mehrotra, Hitesh Sagtani, Dominic Cooney, Rafal Gajdulewicz, Beyang Liu, Julie Tibshirani, Quinn Slack
arXiv ID
2408.05344
Category
cs.IR: Information Retrieval
Cross-listed
cs.LG,
cs.SE
Citations
3
Venue
ACM Conference on Recommender Systems
Last Checked
4 months ago
Abstract
In this work, we discuss a recently popular type of recommender system: an LLM-based coding assistant. Connecting the task of providing code recommendations in multiple formats to traditional RecSys challenges, we outline several similarities and differences due to domain specifics. We emphasize the importance of providing relevant context to an LLM for this use case and discuss lessons learned from context enhancements & offline and online evaluation of such AI-assisted coding systems.
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