Summary of Contextual Augmented Multi-model Programming (camp): a Hybrid Local-cloud Copilot Framework, by Yuchen Wang et al.
Contextual Augmented Multi-Model Programming (CAMP): A Hybrid Local-Cloud Copilot Framework
by Yuchen Wang, Shangxin Guo, Chee Wei Tan
First submitted to arxiv on: 20 Oct 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper presents CAMP, a multi-model AI-assisted programming framework that enables Large Language Models (LLMs) to be integrated into local development environments like those within the Apple ecosystem. The framework employs Retrieval-Augmented Generation (RAG) to retrieve contextual information from the codebase and facilitate context-aware prompt construction, optimizing cloud model performance. This empowers LLMs’ capabilities in local Integrated Development Environments (IDEs). The methodology is actualized in Copilot for Xcode, an AI-assisted programming tool that enables diverse generative programming tasks, including automatic code completion, documentation, error detection, and intelligent user-agent interaction. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary CAMP is a new way to use artificial intelligence to help with coding. It lets Large Language Models (LLMs) work better in special environments like Apple’s Xcode. The AI uses information from the code to make suggestions that are more helpful. This makes it easier for developers to write code and fix mistakes. CAMP has been tested and shows great promise. |
Keywords
» Artificial intelligence » Prompt » Rag » Retrieval augmented generation
