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Understanding Knowledge in Relevance AI

Knowledge in Relevance AI refers to any information that your agents can access and reference when responding to queries or performing tasks. This information can come in various forms:
  • Factual information stored in knowledge bases
  • Contextual examples that demonstrate desired outputs
  • External data sources connected through integrations
  • Website content extracted from specific URLs
Think of knowledge as the reference material your agents consult before formulating responses. Just as a human expert might refer to documentation, databases, or examples before answering a question, your AI agents can leverage knowledge to ground their responses in factual information.

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Finding Knowledge

There are several ways to find and access knowledge within Relevance AI:
  1. Log into your Relevance AI dashboard
  2. Click on “Knowledge” in the main navigation menu
  3. Browse existing knowledge bases or create new ones

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Searching for Specific Knowledge

Within the Knowledge section, you can:
  • Use the search bar to find specific knowledge bases by name or content
  • Filter knowledge bases by type, creation date, or other attributes
  • Sort knowledge bases to quickly locate what you need

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Exploring Knowledge Categories

Knowledge bases in Relevance AI are typically organized into categories based on their purpose:
  • Reference Knowledge: Comprehensive information repositories like FAQs, product details, or company policies
  • Example Knowledge: Samples of desired outputs that demonstrate “what good looks like”
  • Procedural Knowledge: Step-by-step instructions for completing specific tasks
  • External Knowledge: Information retrieved from integrated tools and services

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Using Knowledge with Your Agents

Once you’ve found the knowledge you need, there are several ways to use it with your agents:

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Connecting Knowledge Bases to Agents

  1. Navigate to the agent you want to enhance
  2. Select “Knowledge” from the agent configuration options
  3. Choose the knowledge base(s) you want to connect
  4. Configure retrieval settings to determine how the agent accesses the knowledge

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Providing Contextual Examples

For tasks where you want to guide your agent’s output style or format:
  1. Create a knowledge base with exemplary outputs
  2. Connect this knowledge base to your agent
  3. Include instructions that direct the agent to follow these examples
For instance, if you want your agent to write case studies in a specific format, provide examples of well-crafted case studies in a knowledge base.

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Leveraging Knowledge in Agent Prompts

When designing agent prompts, you can explicitly instruct the agent to reference specific knowledge:
Before responding, please check the knowledge base for information about [specific topic].
This ensures your agent actively seeks relevant information rather than relying solely on its pre-trained knowledge.

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Knowledge Retrieval Methods

Relevance AI offers several methods for retrieving knowledge: Semantic search finds information based on meaning rather than exact keyword matches. This allows agents to retrieve relevant information even when queries use different terminology than what’s in the knowledge base. To optimize for semantic search:
  • Structure your knowledge with clear, descriptive content
  • Include synonyms and alternative phrasings for important concepts
  • Organize related information together for better contextual understanding
Hybrid search combines semantic search with keyword matching for more precise results. This is particularly useful when your knowledge base contains specific terminology or unique identifiers.

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Filtered Retrieval

You can set up filters to narrow down knowledge retrieval based on specific attributes:
  1. Navigate to the knowledge base settings
  2. Configure retrieval filters based on metadata fields
  3. Apply these filters to ensure agents only access relevant information

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Knowledge Types and Their Uses

Different types of knowledge serve different purposes:

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Snippet Knowledge

Small pieces of information that provide specific guidance or examples:
  • Use for: Style guides, tone examples, formatting templates
  • Best for: Shaping how agents respond rather than what they respond with
  • Example: “This is what a professional email response should look like…”

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Database Knowledge

Comprehensive collections of information organized in a structured format:
  • Use for: FAQs, product catalogs, support documentation
  • Best for: Providing detailed, factual information on a wide range of topics
  • Example: A table of product specifications and features

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Integration Knowledge

Information retrieved from external tools and services:
  • Use for: Real-time data, website content, third-party databases
  • Best for: Ensuring agents have access to the most current information
  • Example: Documentation pulled from your company website

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Best Practices for Knowledge Management

To get the most out of knowledge in Relevance AI:

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Organize Knowledge Strategically

  • Create separate knowledge bases for different topics or departments
  • Use clear, descriptive names for easy identification
  • Add metadata to help with filtering and organization

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Update Knowledge Regularly

  • Schedule regular reviews of your knowledge bases
  • Update information as products, policies, or procedures change
  • Remove outdated information to prevent confusion

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Monitor Knowledge Usage

  • Review which knowledge bases are being accessed most frequently
  • Identify gaps where agents might need additional information
  • Refine knowledge based on actual usage patterns

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Balance Comprehensiveness with Relevance

  • Include enough detail for accurate responses
  • Avoid overwhelming knowledge bases with unnecessary information
  • Focus on quality over quantity

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Troubleshooting Knowledge Retrieval

If your agents aren’t effectively using knowledge:

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Check Knowledge Connection

Ensure the knowledge base is properly connected to the agent and that retrieval settings are configured correctly.

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Review Knowledge Content

Examine whether the knowledge base contains the information the agent needs in a format it can effectively use.

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Adjust Retrieval Settings

Modify search parameters, result limits, or relevance thresholds to improve retrieval accuracy.

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Test with Sample Queries

Use the testing interface to see how the agent retrieves and uses knowledge for specific questions.

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FAQs

Q: How much knowledge can I add to my agents? A: Relevance AI supports extensive knowledge bases, but for optimal performance, focus on providing relevant, well-structured information rather than simply maximizing volume. Q: Can I use multiple knowledge bases with a single agent? A: Yes, you can connect multiple knowledge bases to one agent. The agent will search across all connected knowledge bases when responding to queries. Q: How do I know if my agent is using the knowledge effectively? A: Use the agent testing interface to see which knowledge sources are being retrieved for specific queries. You can also review conversation logs to see how knowledge is being incorporated into responses. Q: Can I prioritize certain knowledge over others? A: Yes, you can configure retrieval settings to prioritize specific knowledge bases or types of information based on relevance scores or metadata attributes. Q: What’s the difference between knowledge and tools? A: Knowledge provides information for agents to reference, while tools enable agents to perform actions or access external systems. Knowledge informs what agents know, while tools expand what agents can do.