Key Features
Knowledge Base Creation
Build searchable repositories of your organization’s information
Document Management
Upload, process, and organize documents across formats
RAG Configuration
Fine-tune how AI accesses and uses your knowledge
Agent Creation
Build specialized agents powered by your knowledge
Testing & Validation
Verify accuracy and relevance of responses
Tool Integration
Extend capabilities with specialized tools
Performance Analytics
Track knowledge retrieval effectiveness
Continuous Improvement
Refine and optimize knowledge access over time
Who Uses AI Knowledge?
AI Knowledge serves various roles within organizations:Information specialists and knowledge managers use AI Knowledge to:
- Organize and structure organizational knowledge
- Create accessible knowledge bases
- Maintain and update information
- Monitor knowledge usage and gaps
How AI Knowledge Works
1
Document Ingestion
Upload and process your organization’s documents and content.AI Knowledge supports various document types including:
- PDF files
- Word documents
- PowerPoint presentations
- Excel spreadsheets
- HTML content
- Text files
- Images with text (via OCR)
- And more
2
Knowledge Processing
Documents are processed, chunked, and converted into embeddings.Processing includes:
- Text extraction
- Semantic chunking
- Vector embedding generation
- Metadata extraction
- Optional content enrichment
3
Knowledge Base Creation
Organize and structure processed documents into searchable knowledge bases.Knowledge bases can be:
- Topic-specific
- Department-focused
- Project-based
- Product-centered
- Audience-targeted
4
Agent Configuration
Connect knowledge bases to AI agents and fine-tune retrieval settings.Configuration includes:
- RAG parameters
- Context handling
- Query processing
- Response generation
- Tool integration
5
Deployment & Use
Make knowledge-powered agents available to users through various channels.Deployment options:
- AI Store listing
- Direct sharing
- Integration into applications
- API access
- Embedded experiences
6
Monitoring & Optimization
Track performance and continuously improve knowledge retrieval.Key metrics include:
- Retrieval accuracy
- Query coverage
- User satisfaction
- Knowledge gaps
- Response quality
RAG Overview
What is RAG?
What is RAG?
RAG combines retrieval systems with generative AI to produce responses grounded in specific knowledge:
- A user query is processed to understand the information need
- Relevant documents or passages are retrieved from knowledge bases
- Retrieved information is provided as context to the language model
- The model generates a response that incorporates this specific knowledge
- Grounded in accurate, up-to-date information
- Specific to your organization’s context
- Properly attributed to sources
- Less prone to hallucination or fabrication
Benefits of RAG
Benefits of RAG
Using RAG through AI Knowledge provides several advantages:
- Knowledge Recency: Access the latest information, beyond the model’s training cutoff
- Factual Grounding: Base responses on verified organizational knowledge
- Reduced Hallucinations: Minimize fabricated or incorrect information
- Domain Specificity: Provide expertise in your organization’s unique areas
- Source Attribution: Trace responses back to specific documents and sources
- Knowledge Control: Determine exactly what information the AI can access and use
- Compliance Support: Ensure responses adhere to organizational policies and standards
Knowledge Base Types
AI Knowledge supports different types of knowledge bases to meet various needs:Document Repositories
Collections of structured documents like manuals, reports, and policies
FAQ Collections
Frequently asked questions with curated answers
Product Knowledge
Information about products, specifications, and features
Procedural Guides
Step-by-step instructions and processes
Technical Documentation
Technical details, APIs, and system information
Training Materials
Educational content and learning resources
Research Collections
Scientific papers, studies, and findings
Legal & Compliance
Regulations, policies, and compliance information
Integration with Prisme.ai Products
AI Knowledge works seamlessly with other Prisme.ai products:| Product | Integration |
|---|---|
| AI SecureChat | Access knowledge bases through conversational interface |
| AI Store | Share and discover knowledge-powered agents |
| AI Collection | Combine structured and unstructured data access |
| AI Builder | Create advanced applications with knowledge integration |
| AI Governance | Manage access and security for knowledge resources |
| AI Insights | Track performance of knowledge-based interactions |
Getting Started with AI Knowledge
1
Access AI Knowledge
Navigate to AI Knowledge from your Prisme.ai dashboard.
2
Create your first knowledge base
Click “Create Knowledge Base” to begin the setup process.Follow the guided process to:
- Name your knowledge base
- Define its purpose
- Set access permissions
- Configure basic settings
3
Upload initial documents
Add documents to your knowledge base.You can:
- Upload files directly
- Connect to document repositories
- Import from URLs
- Use the document processing API
4
Configure basic RAG settings
Set up how your knowledge will be processed and retrieved.
Start with recommended settings for:
- Chunking strategy
- Retrieval method
- Context handling
- Response generation
5
Create a knowledge agent
Build an agent that uses your knowledge base.Define:
- Agent purpose and behavior
- Knowledge base connections
- Response preferences
- User interaction style
6
Test and refine
Try out your agent and adjust settings as needed.The testing interface allows you to:
- Ask sample questions
- View retrieved contexts
- Evaluate response quality
- Identify areas for improvement
Use Cases
Internal Knowledge Management
Create agents that help employees find and use company policies, procedures, and best practices.
Example: HR Policy Assistant that answers questions about benefits, time off, and workplace policies.
Technical Support
Build agents that assist with troubleshooting, maintenance, and technical information.
Example: Product Support Agent that helps customers resolve issues with specific products.
Research Assistance
Develop agents that help analyze and extract insights from research collections.
Example: Research Assistant that helps scientists navigate relevant papers and studies.
Training & Onboarding
Create agents that help with learning and skill development.
Example: Onboarding Assistant that guides new employees through company systems and processes.
Sales & Marketing Support
Build agents that provide accurate product information and marketing content.
Example: Product Knowledge Agent that helps sales teams access accurate specifications and features.
Compliance & Legal
Develop agents that help navigate complex regulatory and legal requirements.
Example: Compliance Assistant that provides guidance on industry regulations and internal policies.
Next Steps
Explore more detailed guides for AI Knowledge features:Knowledge Bases
Create and manage collections of organizational knowledge
Document Management
Upload, process, and organize documents
Create Knowledge Base
Step-by-step guide to building your first knowledge base
RAG Configuration
Fine-tune retrieval and response settings
Analytics
Track and improve knowledge base performance
Tools Integration
Enhance agents with specialized capabilities
Agent Testing
Validate and improve agent responses
Advanced RAG
Implement sophisticated RAG architectures