Training Guide for YourGPT Chatbot

Welcome to YourGPT’s training guide. This document will help you effectively train your chatbot to provide accurate and helpful responses to your users. We will cover key strategies such as using structured data, FAQs, and best practices for formatting your training data.

Start Training Your Chatbot

We allow you to train your chatbot with your own bussiness data.

Training Options Choose from a variety of training options to train your AI chatbot effectively.

Follow these guidelines to train your YourGPT chatbot effectively and ensure it provides the best possible experience.

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Best Practices for Training

Practice	Description
Consistency	Use a consistent format for all training data.
Clarity	Ensure questions and answers are clear and to the point.
Comprehensiveness	Cover as many potential user queries as possible in your training data. The more the chatbot is trained on, the better it will be at handling a wide range of user queries.
Accuracy	Ensure that the training data is accurate and up to date.
Periodic Updates	Periodically update the training data to reflect new information.
Regular Testing	Routinely test the chatbot to verify it’s providing accurate responses.

Note

Big documents are further divided into smaller chunks. The default chunk size is 1024 characters, with an overlap of 200 characters. When creating documents, add one of the following separators after every 1000 characters: “\n~~~\n”, otherwise other common “\n\n\n”, “\n\n”, “\n” etc will be used as separators.

What is Match Score?

The match score measures how closely the training data aligns with the user’s query. It helps identify the most relevant knowledge base node. A higher match score indicates greater relevance with knowledge base node.

The score is calculated by comparing the user’s query with the training data, considering factors such as:

• Semantic understanding: The score considers the meaning and context of the query, not just exact keyword matches
• Vector proximity: Technically, the score often represents the cosine similarity between query and document vectors in the embedding space
• Contextual relevance: How well the entire query aligns with the document’s overall topic and focus
• Relative ranking: The absolute score matters less than how documents rank compared to each other

The match score ranges from 0 to 1 and is generated by embedding models. Different models use different scoring mechanisms, so some may produce consistently higher scores, while others may yield lower scores.

To review your AI’s training data, check out the Training View Source docs. This allows you to inspect the match score for the knowledge base nodes used to train your AI.

Additional Training Tips

Be Specific Make your training pairs specific to common scenarios you want to handle

Include Variations Add different ways users might ask the same question

Keep It Clear Write responses exactly as you want the AI to communicate

Stay Consistent Maintain the same tone and policy across all responses

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Training with FAQs

FAQs are an excellent method for providing the chatbot with frequently asked questions and their corresponding answers. This helps the chatbot to promptly address user queries.

• Identify the most common questions from your users.
• Provide clear and concise answers to each question.
• Use Website to FAQ feature to quickly generate FAQ form website content