Generate Synthetic Data from Personas to Train AI Chatbots
Using Personas and Efficient Inference to Create Targeted Training Data for AI Chatbots
When fine-tuning large language models (LLMs) to train an AI chatbot, the quality of your fine-tuning dataset is the most crucial factor in determining whether your chatbot will excel in its target task.
However, sourcing a suitable dataset can be challenging. Your company’s or personal data may be too limited, while public datasets are often too broad or too narrowly focused. A popular solution is to generate custom datasets using LLMs to train an AI chatbot effectively on data that truly fits your needs.
For instance, if your goal is to develop a chatbot that can answer questions across various fields, generating a training dataset can save you the time and effort of gathering data from multiple sources and standardizing its format, style, and tone.
In this article, we’ll explore how to generate thousands of question-answer pairs. Using FinePersonasV0.1 to prompt Qwen2.5, we’ll create synthetic questions and answers in various domains and for various personas.
The following notebook implements all the steps to generate a dataset:
In a follow-up article, we’ll see how to format and use this dataset to train an AI chatbot.
Preparing Prompts with Fine Personas
Our goal is to fine-tune an LLM to be a chatbot capable of answering questions in a wide variety of domains, with an educational tone.