Core Architecture Layers
Ingestion
Documents, tickets, PDFs, CRM notes, and product knowledge enter the system through structured pipelines.
Transformation
Chunking, metadata tagging, normalization, and deduplication improve downstream retrieval.
Retrieval
Vector search, hybrid search, reranking, and permission filtering should work together.
Generation + evaluation
Prompts, citations, scoring, feedback, and experiments help improve answer quality safely.
Rule: retrieval quality is usually a bigger bottleneck than the model itself.
What a Good Retrieval Pipeline Looks Like
- Chunk content based on meaning, not only by character count.
- Store metadata such as source, department, confidentiality, and freshness.
- Apply permission filtering before or during retrieval.
- Rerank retrieved items before prompt assembly.
- Log retrieval score, citation usage, and failure cases.
Common Enterprise Requirements
- Tenant isolation or department isolation
- Freshness for fast-changing knowledge
- Document source traceability
- Evaluation dataset for major tasks
Examples
Example: Internal Support Assistant
- Sources: Notion docs, Jira tickets, runbooks, Slack summaries
- Access: team-based permissions
- Goal: answer “how do I fix X?” with citations and escalation paths
Example: Sales Enablement RAG
- Sources: case studies, proposal templates, pricing notes, competitor docs
- Goal: faster proposal drafting with approved claims and current positioning
Production Launch Checklist
- Permission-aware retrieval tested with sensitive docs
- Golden questions and expected answers prepared
- Monitoring for hallucinations, low retrieval scores, and empty context
- Clear human fallback for low-confidence scenarios
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