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Contextual Augmented Multi-Model Programming (CAMP): A Hybrid Local-Cloud Copilot Framework

arXiv 2410.15285 ↗First posted 20 Oct 2024cs.AIcited by 0

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Abstract (arXiv)

The advancements in cloud-based Large Languages Models (LLMs) have revolutionized AI-assisted programming. However, their integration into certain local development environments like ones within the Apple software ecosystem (e.g., iOS apps, macOS) remains challenging due to computational demands and sandboxed constraints. This paper presents CAMP, a multi-model AI-assisted programming framework that consists of a local model that employs Retrieval-Augmented Generation (RAG) to retrieve contextual information from the codebase to facilitate context-aware prompt construction thus optimizing the performance of the cloud model, empowering LLMs' capabilities in local Integrated Development Environments (IDEs). The methodology is actualized in Copilot for Xcode, an AI-assisted programming tool crafted for Xcode that employs the RAG module to address software constraints and enables diverse generative programming tasks, including automatic code completion, documentation, error detection, and intelligent user-agent interaction. The results from objective experiments on generated code quality and subjective experiments on user adoption collectively demonstrate the pilot success of the proposed system and mark its significant contributions to the realm of AI-assisted programming.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 22 of its 64 references that are in the map. First posted on arXiv 20 Oct 2024.

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22 of 64 references.

References · 22

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10 more
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  2. 2025SpareCodeSearch: Searching for Code Context When You Have No Spare GPU
  3. 2025From Code Generation to Software Testing: AI Copilot with Context-Based RAG
  4. 2024Retrieval-augmented code completion for local projects using large language models
  5. 2025Context Engineering for Multi-Agent LLM Code Assistants Using Elicit, NotebookLM, ChatGPT, and Claude Code
  6. 2025Generative AI and the Transformation of Software Development Practices
  7. 2023Exploring Parameter-Efficient Fine-Tuning Techniques for Code Generation with Large Language Models
  8. 2025An Empirical Study of Retrieval-Augmented Code Generation: Challenges and Opportunities