RAG Knowledge Base

Retrieval Augmented Generation for accurate AI responses

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

• Chat Assistant
• Agent Management