The Kaitchup – AI on a Budget

The Kaitchup – AI on a Budget

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The Kaitchup – AI on a Budget
The Kaitchup – AI on a Budget
Generate Synthetic Data from Personas to Train AI Chatbots
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Generate Synthetic Data from Personas to Train AI Chatbots

Using Personas and Efficient Inference to Create Targeted Training Data for AI Chatbots

Benjamin Marie's avatar
Benjamin Marie
Oct 10, 2024
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The Kaitchup – AI on a Budget
The Kaitchup – AI on a Budget
Generate Synthetic Data from Personas to Train AI Chatbots
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Generated with DALL-E

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.

The Kaitchup – AI on a Budget is a reader-supported publication. To receive new posts and support my work, consider becoming a free or paid subscriber.

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:

Get the notebook (#111)

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.

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