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Coverage focus: AI · Banking, Healthcare, Government · Technical Deep-Dive.
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Artificial Intelligence•6 min•Trufe Insights•Apr 4, 2026
RAG Architecture for Enterprise — Beyond the Tutorial, Into Production
Technical Deep-Dive perspective for Banking, Healthcare, Government with implementation guidance and internal references.
Why Every Enterprise Wants RAG (And Why Most Implementations Fail)
Production RAG Architecture
- Document ingestion pipeline: chunking strategies, metadata extraction
- Embedding models: open-source vs. commercial, fine-tuning for domain
- Vector databases: Pinecone, Weaviate, Qdrant, pgvector — honest comparison
- Retrieval: hybrid search (dense + sparse), re-ranking, query expansion
- Generation: prompt engineering, citation grounding, hallucination mitigation
The Security Layer Nobody Builds
- Access control on retrieved documents (row-level permissions in vector DBs)
- PII detection and redaction in retrieved context
- Audit trails: what was retrieved, what was generated, who asked
- Data residency: where embeddings live matters for DPDPA/GDPR
Evaluation and Monitoring
- RAG-specific metrics: faithfulness, relevance, context precision
- Drift detection in retrieval quality over time
- Human feedback loops
Case Example: Internal Knowledge Base for a Bank
Closing CTA:
→ Link to: /solutions/artificial-intelligence/ai-solutions/nlp/
→ Link to: /solutions/artificial-intelligence/machine-learning/custom-algorithms/
Internal References
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