RAILS: Retrieval-Augmented Intelligence for Learning Software Development

TL;DR

RAILS enhances large language models for Java software development by integrating semantic retrieval and iterative validation, significantly improving code accuracy and import suggestions in real-world scenarios.

Contribution

The paper introduces RAILS, a retrieval-augmented framework that improves LLM-based Java coding assistance through semantic context retrieval and compiler-guided validation.

Findings

RAILS outperforms baseline prompts in Java import error cases.

RAILS maintains intent and reduces hallucinations.

Effective in diverse real-world Java scenarios.

Abstract

Large Language Models (LLMs) like GPT-3.5-Turbo are increasingly used to assist software development, yet they often produce incomplete code or incorrect imports, especially when lacking access to external or project-specific documentation. We introduce RAILS (Retrieval-Augmented Intelligence for Learning Software Development), a framework that augments LLM prompts with semantically retrieved context from curated Java resources using FAISS and OpenAI embeddings. RAILS incorporates an iterative validation loop guided by compiler feedback to refine suggestions. We evaluated RAILS on 78 real-world Java import error cases spanning standard libraries, GUI APIs, external tools, and custom utilities. Despite using the same LLM, RAILS outperforms baseline prompting by preserving intent, avoiding hallucinations, and surfacing correct imports even when libraries are unavailable locally. Future work will integrate symbolic filtering via PostgreSQL and extend support to other languages and IDEs.