# A Multi-agent Onboarding Assistant based on Large Language Models, Retrieval Augmented Generation, and Chain-of-Thought

arXiv: https://arxiv.org/abs/2503.23421 · HTML: https://mlsanity.com/paper/2503.23421/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Placed in Reasoning for agents, planning and code, one of 17 directions in LLM reasoning.
- Builds on 7 of its 16 references that are in the map.
- First posted on arXiv 30 Mar 2025.

## Placed in

- LLM reasoning / [Reasoning for agents, planning and code](https://mlsanity.com/lr/reasoning-for-agents-planning-and-code/)
- Retrieval-augmented generation / [RAG for code](https://mlsanity.com/rag/rag-for-code/)
- LLM agents and tool use / [Software engineering coding agents](https://mlsanity.com/agents/software-engineering-coding-agents/)

## Builds on (most cited first, 7 of 7)

- [Chain-of-Thought Prompting Elicits Reasoning in Large Language Models](https://mlsanity.com/paper/2201.11903/) (2022)
- [Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks](https://mlsanity.com/paper/2005.11401/) (2020)
- [Automatic Chain of Thought Prompting in Large Language Models](https://mlsanity.com/paper/2210.03493/) (2022)
- [Leveraging Large Language Models for Enhancing the Understandability of Generated Unit Tests](https://mlsanity.com/paper/2408.11710/) (2024)
- [A Case Study of Onboarding in Software Teams: Tasks and Strategies](https://mlsanity.com/paper/2103.05055/) (2021)
- [Effective team onboarding in Agile software development: techniques and goals](https://mlsanity.com/paper/1907.10206/) (2019)
- [Developer Needs and Feasible Features for AI Assistants in IDEs](https://mlsanity.com/paper/2410.08676/) (2024)

## Built on by (most cited first, 2 of 2)

- [Prompt-with-Me: in-IDE Structured Prompt Management for LLM-Driven Software Engineering](https://mlsanity.com/paper/2509.17096/) (2025)
- [Evaluating Large Language Models for Functional and Maintainable Code in Industrial Settings: A Case Study at ASML](https://mlsanity.com/paper/2509.12395/) (2025)

## Abstract (arXiv)

Effective onboarding in software engineering is crucial but difficult due to the fast-paced evolution of technologies. Traditional methods, like exploration and workshops, are costly, time-consuming, and quickly outdated in large projects. We propose the Onboarding Buddy system, which leverages large language models, retrieval augmented generation, and an automated chain-of-thought approach to improve onboarding. It integrates dynamic, context-specific support within the development environment, offering natural language explanations, code insights, and project guidance. Our solution is agent-based and provides customized assistance with minimal human intervention. Our study results among the eight participants show an average helpfulness rating of (M=3.26, SD=0.86) and ease of onboarding at (M=3.0, SD=0.96) out of four. While similar to tools like GitHub Copilot, Onboarding Buddy uniquely integrates a chain-of-thought reasoning mechanism with retrieval-augmented generation, tailored specifically for dynamic onboarding contexts. While our initial evaluation is based on eight participants within one project, we will explore larger teams and multiple real-world codebases in the company to demonstrate broader applicability. Overall, Onboarding Buddy holds great potential for enhancing developer productivity and satisfaction. Our tool, source code, and demonstration video are publicly available