# PersonaBOT: Bringing Customer Personas to Life with LLMs and RAG

arXiv: https://arxiv.org/abs/2505.17156 · HTML: https://mlsanity.com/paper/2505.17156/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 38 references that are in the map.
- First posted on arXiv 22 May 2025.

## Builds on (most cited first, 18 of 18)

- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [GPT-4 Technical Report](https://mlsanity.com/paper/2303.08774/) (2023)
- [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)
- [MultiHop-RAG: Benchmarking Retrieval-Augmented Generation for Multi-Hop Queries](https://mlsanity.com/paper/2401.15391/) (2024)
- [Two Tales of Persona in LLMs: A Survey of Role-Playing and Personalization](https://mlsanity.com/paper/2406.01171/) (2024)
- [Marked Personas: Using Natural Language Prompts to Measure Stereotypes in Language Models](https://mlsanity.com/paper/2305.18189/) (2023)
- [Retrieval-Augmented Generation with Graphs (GraphRAG)](https://mlsanity.com/paper/2501.00309/) (2024)
- [Personalization of Large Language Models: A Survey](https://mlsanity.com/paper/2411.00027/) (2024)
- [CloChat: Understanding How People Customize, Interact, and Experience Personas in Large Language Models](https://mlsanity.com/paper/2402.15265/) (2024)
- [Persona-L has Entered the Chat: Leveraging LLM and Ability-based Framework for Personas of People with Complex Needs](https://mlsanity.com/paper/2409.15604/) (2024)
- [A Review on Generative AI Models for Synthetic Medical Text, Time Series, and Longitudinal Data](https://mlsanity.com/paper/2411.12274/) (2024)
- [Improved prompting and process for writing user personas with LLMs, using qualitative interviews: Capturing behaviour and personality traits of users](https://mlsanity.com/paper/2310.06391/) (2023)
- [PersonaGen: A Tool for Generating Personas from User Feedback](https://mlsanity.com/paper/2307.00390/) (2023)
- [Automating Customer Needs Analysis: A Comparative Study of Large Language Models in the Travel Industry](https://mlsanity.com/paper/2404.17975/) (2024)
- [A Reality check of the benefits of LLM in business](https://mlsanity.com/paper/2406.10249/) (2024)
- [SOMONITOR: Combining Explainable AI & Large Language Models for Marketing Analytics](https://mlsanity.com/paper/2407.13117/) (2024)
- [LLMs' ways of seeing User Personas](https://mlsanity.com/paper/2409.14858/) (2024)

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

- [POPI: Personalizing LLMs via Optimized Natural Language Preference Inference](https://mlsanity.com/paper/2510.17881/) (2025)
- [Your Reviews Replicate You: LLM-Based Agents as Customer Digital Twins for Conjoint Analysis](https://mlsanity.com/paper/2604.22756/) (2026)

## Abstract (arXiv)

The introduction of Large Language Models (LLMs) has significantly transformed Natural Language Processing (NLP) applications by enabling more advanced analysis of customer personas. At Volvo Construction Equipment (VCE), customer personas have traditionally been developed through qualitative methods, which are time-consuming and lack scalability. The main objective of this paper is to generate synthetic customer personas and integrate them into a Retrieval-Augmented Generation (RAG) chatbot to support decision-making in business processes. To this end, we first focus on developing a persona-based RAG chatbot integrated with verified personas. Next, synthetic personas are generated using Few-Shot and Chain-of-Thought (CoT) prompting techniques and evaluated based on completeness, relevance, and consistency using McNemar's test. In the final step, the chatbot's knowledge base is augmented with synthetic personas and additional segment information to assess improvements in response accuracy and practical utility. Key findings indicate that Few-Shot prompting outperformed CoT in generating more complete personas, while CoT demonstrated greater efficiency in terms of response time and token usage. After augmenting the knowledge base, the average accuracy rating of the chatbot increased from 5.88 to 6.42 on a 10-point scale, and 81.82% of participants found the updated system useful in business contexts.