What is Knowledge?
Knowledge is Relevance AI’s retrieval augmented generation (RAG) system. It lets you give your agents and tools access to specific information — your docs, your data, your expertise — so they can respond with accuracy instead of relying only on their training data. Think of it as your agent’s reference library. When an agent needs to answer a question or complete a task, it searches your knowledge bases for the most relevant information and uses it to ground its response.What are the benefits of Knowledge?
More accurate responses
Agents reference your actual data instead of guessing. Fewer hallucinations, more reliable output.
Domain expertise on demand
Turn any agent into a specialist by giving it access to the right knowledge — product docs, playbooks, policies, whatever it needs.
Always up to date
Update your knowledge bases as things change. Your agents automatically use the latest information.
Works across your stack
Pull in data from files, websites, Google Drive, SharePoint, Notion, and more.
How Knowledge Works
Knowledge uses semantic search to find the most relevant information for each query. When an agent receives a task or question:- It searches your connected knowledge bases for relevant content
- The most relevant results are passed to the agent as context
- The agent uses that context to generate an accurate, grounded response
Connecting Knowledge to an agent
When you add a knowledge base to an agent, you choose how the agent uses it:- Add all to prompt — the entire knowledge set is included in the prompt every time the agent runs. Best for small or simple datasets.
- Allow agent to search — the agent searches the knowledge base using RAG and pulls only what’s relevant. Best for large or complex datasets.
Where is Knowledge most useful?
Knowledge is most effective when your agents need access to specific, proprietary, or frequently changing information:- Customer support: Product documentation, troubleshooting guides, and FAQs so agents resolve issues accurately
- Sales enablement: Battlecards, pricing sheets, and case studies to keep sales agents informed
- Internal operations: Company policies, SOPs, and handbooks that employees need on demand
- Onboarding: Training materials and process documentation for new team members
- Research and analysis: Reports, datasets, and reference materials for agents that synthesize information
How to get the most from Knowledge?
- Keep data clean and structured: Well-organized content with clear headings and sections improves retrieval accuracy.
- Be specific with what you upload: Give agents only the knowledge they need — too much irrelevant data dilutes search results.
- Keep it up to date: Regularly refresh your knowledge bases so agents always reference current information.
- Use enrichment: Run tools over your knowledge to add summaries, tags, or derived fields that help agents find the right content faster.
- Use snippets for quick access: Set up snippets for frequently referenced information your agents use often.
Frequently asked questions (FAQs)
What file types can I upload to Knowledge?
What file types can I upload to Knowledge?
You can upload CSV, PDF, Excel, JSON, and audio files. You can also pull content from websites or sync from integrations like Google Drive, SharePoint, and Notion.
How does semantic search differ from keyword search?
How does semantic search differ from keyword search?
Keyword search looks for exact word matches. Semantic search understands meaning — so a search for “pricing information” can find content titled “how much does it cost” even though the words don’t match.
Can multiple agents share the same knowledge base?
Can multiple agents share the same knowledge base?
How do I keep my knowledge base up to date?
How do I keep my knowledge base up to date?
You can manually update entries, re-upload files, or use integrations like Google Drive and SharePoint that sync automatically when your source documents change.
What is knowledge enrichment?
What is knowledge enrichment?
Enrichment lets you run tools over your knowledge table to add or transform data — for example, generating summaries, extracting key fields, or tagging entries. This makes retrieval more effective and gives agents richer context.