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.
Follow these guidelines to train your YourGPT chatbot effectively and ensure it provides the best possible experience.
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. |
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
- 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 from website content