Skip to main content

​
What is RAG?

RAG = Retrieval Augmented Generation Instead of AI making up answers, it retrieves relevant documents and uses those to generate responses.

​
How RAG Works

Without RAG (hallucination risk): 
Question: "What's our conveyor belt maintenance schedule?"
AI thinks: "I think we maintain conveyors monthly..."
Result: Guessed answer (might be wrong!)
With RAG (grounded in facts): 
Question: "What's our conveyor belt maintenance schedule?"
System retrieves: "Conveyor maintenance schedule.pdf"
AI reads: "Monthly oil changes, quarterly belt inspection..."
Result: Accurate answer grounded in documentation

​
RAG Benefits

✅ Accuracy: Answers grounded in real documents ✅ Consistency: Same procedures every time ✅ Current: Uses latest documentation ✅ Traceable: Can cite sources ✅ Preventive: Reduces hallucinations

​
Knowledge Base Structure

​
By Department

Quality Department
  • Quality control procedures
  • Defect classification guide
  • Testing standards
  • Historical resolutions
Maintenance Department
  • Equipment manuals
  • Maintenance schedules
  • Troubleshooting guides
  • Parts specifications
Safety Department
  • Safety procedures
  • Emergency response plans
  • OSHA standards
  • Incident protocols
Logistics Department
  • Shipping procedures
  • Supplier contacts
  • Inventory management
  • Delivery standards

​
By Document Type

Procedures
  • Step-by-step instructions
  • Standard operating procedures
  • Process flows
  • Checklists
Guides
  • Troubleshooting guides
  • How-to documents
  • Best practices
  • Training materials
Reference
  • Equipment specs
  • Part numbers
  • Contact information
  • Standards documents
Historical
  • Past resolutions
  • Case studies
  • Lessons learned
  • Success stories

​
Building Knowledge Base

​
Gathering Documents

  1. Existing documentation:
    • Company procedures
    • Training materials
    • Equipment manuals
    • Policy documents
  2. Historical data:
    • Past resolved claims (the “why” and solution)
    • Maintenance logs
    • Quality records
    • Safety incidents
  3. External resources:
    • Industry standards
    • OSHA guidelines
    • Supplier documentation
    • Regulatory requirements

​
Tagging Documents

Add metadata for search:
  • Department: Quality, Maintenance, Safety, Logistics
  • Category: Procedure, Guide, Reference, Historical
  • Tags: Conveyor, Hydraulic, Safety, etc.
  • Last updated: Date
  • Version: Current, Archived

​
Using RAG in Agents

​
Automatic Retrieval

When user asks agent question:
  1. System searches knowledge base
  2. Finds relevant documents (usually top 3-5)
  3. Passes to AI along with question
  4. AI uses documents to generate response
  5. Provides citation: “Per Maintenance Schedule v2.3”

​
Citation & Traceability

Responses include:
  • Direct answers
  • Source documents
  • Relevant procedures
  • Links to full documents
Example: 
Q: "How often should we calibrate the press?"
A: "Every 30 days per Equipment Manual Section 4.2.
   Last calibration: Jan 15, 2025.
   Next due: Feb 15, 2025.
   See: press_calibration_guide.pdf"

​
Maintaining Knowledge Base

​
Monthly Review

  • Update changed procedures
  • Add new learnings from recent claims
  • Fix broken links
  • Update contact information

​
Quarterly Audit

  • Verify all procedures still current
  • Identify gaps (questions not answerable)
  • Remove obsolete documents
  • Reorganize if needed

​
When Processes Change

  • Update relevant documents IMMEDIATELY
  • Notify team of changes
  • Retrain agents if needed
  • Archive old versions

​
Knowledge Base Quality

Good knowledge base indicators:
  • ✅ Agents provide accurate answers
  • ✅ Users find helpful information
  • ✅ Few “I don’t know” responses
  • ✅ Consistent procedures
  • ✅ Up-to-date information
Poor knowledge base indicators:
  • ❌ Agents struggle to answer questions
  • ❌ Users can’t find information
  • ❌ Conflicting procedures documented
  • ❌ Outdated information causing errors
  • ❌ Gaps in coverage

​
Improving RAG Performance

If AI doesn’t find answers:
  • Add missing documents
  • Improve document titles and tags
  • Break large documents into sections
  • Add more examples
If AI gives wrong answers:
  • Review and update procedures
  • Remove conflicting documents
  • Clarify ambiguous instructions
  • Add context to procedures

​
Next Steps