Our Retrieval Augmented Generation (RAG) solution
Knowledge in Relevance AI serves as the foundation for your AI agents’ intelligence, enabling them to access relevant information when responding to queries or performing tasks. This guide will walk you through the process of creating and organizing knowledge in Relevance AI, empowering your agents with the context they need to deliver accurate, helpful responses.
Knowledge in Relevance AI is a powerful Retrieval Augmented Generation (RAG) solution that provides your AI agents with access to specific information beyond their pre-trained knowledge. By creating well-organized knowledge bases, you can:
Knowledge in Relevance AI can take many forms - from small snippets of information that guide your agents on what “good” looks like, to comprehensive databases of support FAQs, to connections with external tools and integrations that retrieve information from specific websites or documentation.
Relevance AI offers multiple flexible approaches to create knowledge, accommodating various information types and sources:
When you have specific information that isn’t contained in existing documents, you can manually create and structure your knowledge:
This approach gives you complete control over the structure and content of your knowledge base, allowing you to create precisely tailored information resources.
When your information already exists in document form, you can quickly import it:
Relevance AI automatically processes these documents, extracting the relevant information and making it searchable for your agents.
To incorporate information from websites:
This method is perfect for incorporating information from your company website, documentation sites, or other online resources without manually copying information.
To sync data from external platforms:
By connecting to third-party platforms, you ensure your agents have access to the most current information across your entire technology stack.
Effective knowledge organization is crucial for ensuring your agents can quickly find and use the right information. Here are strategies for organizing your knowledge in Relevance AI:
For FAQ-style knowledge or structured data:
When importing documents:
Group related knowledge bases into collections:
To maximize the effectiveness of your knowledge bases:
Add contextual metadata to your knowledge entries:
Implement processes to ensure accuracy:
Enhance retrievability with semantic approaches:
Once your knowledge is created and organized, connect it to your agents:
Fine-tune how your agents interact with knowledge:
Adjust how your agent searches for information:
When using multiple knowledge bases:
Enhance retrieval effectiveness:
To maintain effective knowledge bases over time:
Our Retrieval Augmented Generation (RAG) solution
Knowledge in Relevance AI serves as the foundation for your AI agents’ intelligence, enabling them to access relevant information when responding to queries or performing tasks. This guide will walk you through the process of creating and organizing knowledge in Relevance AI, empowering your agents with the context they need to deliver accurate, helpful responses.
Knowledge in Relevance AI is a powerful Retrieval Augmented Generation (RAG) solution that provides your AI agents with access to specific information beyond their pre-trained knowledge. By creating well-organized knowledge bases, you can:
Knowledge in Relevance AI can take many forms - from small snippets of information that guide your agents on what “good” looks like, to comprehensive databases of support FAQs, to connections with external tools and integrations that retrieve information from specific websites or documentation.
Relevance AI offers multiple flexible approaches to create knowledge, accommodating various information types and sources:
When you have specific information that isn’t contained in existing documents, you can manually create and structure your knowledge:
This approach gives you complete control over the structure and content of your knowledge base, allowing you to create precisely tailored information resources.
When your information already exists in document form, you can quickly import it:
Relevance AI automatically processes these documents, extracting the relevant information and making it searchable for your agents.
To incorporate information from websites:
This method is perfect for incorporating information from your company website, documentation sites, or other online resources without manually copying information.
To sync data from external platforms:
By connecting to third-party platforms, you ensure your agents have access to the most current information across your entire technology stack.
Effective knowledge organization is crucial for ensuring your agents can quickly find and use the right information. Here are strategies for organizing your knowledge in Relevance AI:
For FAQ-style knowledge or structured data:
When importing documents:
Group related knowledge bases into collections:
To maximize the effectiveness of your knowledge bases:
Add contextual metadata to your knowledge entries:
Implement processes to ensure accuracy:
Enhance retrievability with semantic approaches:
Once your knowledge is created and organized, connect it to your agents:
Fine-tune how your agents interact with knowledge:
Adjust how your agent searches for information:
When using multiple knowledge bases:
Enhance retrieval effectiveness:
To maintain effective knowledge bases over time: