RAG Knowledge Base for Customer and Internal Support
A case study in designing RAG systems around trusted knowledge sources, document structure, retrieval quality, and support workflow fit.
What this demonstrates
RAG knowledge architecture layers
Conceptual visual overview
This is a conceptual representation of the architecture or workflow, not a full production diagram.
AI + Data + Salesforce Architecture
Conceptual system layers
Business workflows
AI / LLM layer
RAG / knowledge layer
Data engineering layer
Salesforce / CRM / MarTech
Analytics and activation
Problem
LLMs can produce confident but incorrect answers when they are disconnected from governed content, source metadata, and the workflow context of the person asking the question.
Approach
Designed a RAG approach that organizes source documents, metadata, embedding strategy, retrieval ranking, prompt grounding, and human review paths before exposing answers in support workflows.
Architecture
The architecture separates content ingestion, metadata tagging, embedding generation, vector retrieval, answer generation, source citation, and feedback capture so each layer can be evaluated independently.
Tools
Outcome
- Improved trust in AI-generated answers
- Created a repeatable pattern for customer and internal support use cases
- Made knowledge gaps visible before AI rollout
- Reduced the risk of deploying a generic chatbot disconnected from business context
Lessons learned
- RAG quality depends on content structure, metadata, and evaluation as much as model choice.
- Support use cases need clear escalation paths when retrieval confidence is low.