# StackRAG Agent: Improving Developer Answers with Retrieval-Augmented Generation

arXiv: https://arxiv.org/abs/2406.13840 · HTML: https://mlsanity.com/paper/2406.13840/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Placed in Agentic RAG, agents and memory, one of 15 directions in Retrieval-augmented generation.
- Builds on 11 of its 16 references that are in the map.
- First posted on arXiv 19 Jun 2024.

## Placed in

- Retrieval-augmented generation / [Agentic RAG, agents and memory](https://mlsanity.com/rag/agentic-rag-agents-and-memory/)
- LLM agents and tool use / [Web, search, and research agents](https://mlsanity.com/agents/web-search-and-research-agents/)

## Builds on (most cited first, 11 of 11)

- [Large Language Models for Software Engineering: A Systematic Literature Review](https://mlsanity.com/paper/2308.10620/) (2023)
- [Challenges and Applications of Large Language Models](https://mlsanity.com/paper/2307.10169/) (2023)
- [RepairAgent: An Autonomous, LLM-Based Agent for Program Repair](https://mlsanity.com/paper/2403.17134/) (2024)
- [Large Language Models for Software Engineering: Survey and Open Problems](https://mlsanity.com/paper/2310.03533/) (2023)
- [PaperQA: Retrieval-Augmented Generative Agent for Scientific Research](https://mlsanity.com/paper/2312.07559/) (2023)
- [RepoAgent: An LLM-Powered Open-Source Framework for Repository-level Code Documentation Generation](https://mlsanity.com/paper/2402.16667/) (2024)
- [PTM4Tag: Sharpening Tag Recommendation of Stack Overflow Posts with Pre-trained Models](https://mlsanity.com/paper/2203.10965/) (2022)
- [Representation Learning for Stack Overflow Posts: How Far are We?](https://mlsanity.com/paper/2303.06853/) (2023)
- [Attention-based model for predicting question relatedness on Stack Overflow](https://mlsanity.com/paper/2103.10763/) (2021)
- [I Know What You Are Searching For: Code Snippet Recommendation from Stack Overflow Posts](https://mlsanity.com/paper/2210.15845/) (2022)
- [Studying and Recommending Information Highlighting in Stack Overflow Answers](https://mlsanity.com/paper/2401.01472/) (2024)

## Built on by (most cited first, 1 of 1)

- [Engineering RAG Systems for Real-World Applications: Design, Development, and Evaluation](https://mlsanity.com/paper/2506.20869/) (2025)

## Abstract (arXiv)

Developers spend much time finding information that is relevant to their questions. Stack Overflow has been the leading resource, and with the advent of Large Language Models (LLMs), generative models such as ChatGPT are used frequently. However, there is a catch in using each one separately. Searching for answers is time-consuming and tedious, as shown by the many tools developed by researchers to address this issue. On the other, using LLMs is not reliable, as they might produce irrelevant or unreliable answers (i.e., hallucination). In this work, we present StackRAG, a retrieval-augmented Multiagent generation tool based on LLMs that combines the two worlds: aggregating the knowledge from SO to enhance the reliability of the generated answers. Initial evaluations show that the generated answers are correct, accurate, relevant, and useful.