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Why Data Sovereignty Matters for Banking AI
A practical guide for CIOs, CISOs, and heads of innovation on deploying AI in banks under Central Bank regulations in Egypt, the UAE, and wider MENA.
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Introduction to RAG
Retrieval-Augmented Generation (RAG) has emerged as the dominant pattern for enterprise AI applications. By combining the power of large language models with domain-specific knowledge retrieval, RAG systems deliver accurate, contextual, and up-to-date responses.
Core RAG Architecture
Components Overview
A production RAG system consists of several key components:
1**Document Ingestion Pipeline**: Processing and preparing source documents
2**Embedding Generation**: Converting text to vector representations
3**Vector Database**: Storing and retrieving embeddings efficiently
4**Retrieval Engine**: Finding relevant context for queries
5**LLM Integration**: Generating responses using retrieved context
6**Response Pipeline**: Post-processing and delivery
Document Ingestion Best Practices
Chunking Strategies
The way documents are chunked significantly impacts retrieval quality:
Fixed-Size Chunking
Simple to implement
Consistent chunk sizes
May break semantic units
Semantic Chunking
Preserves meaning
Variable sizes
More complex implementation
Hierarchical Chunking
Parent-child relationships
Enables context expansion
Better for complex documents
Recommended Approach
For enterprise documents, we recommend:
Chunk size: 512–1024 tokens
Overlap: 10–20% between chunks
Metadata: Preserve document structure information
Preprocessing: Clean and normalize text
Vector Database Selection
Key Considerations
When selecting a vector database:
1**Scale**: Number of vectors and query volume
2**Latency**: Response time requirements
3**Features**: Filtering, hybrid search capabilities
4**Deployment**: Cloud, on-premise, or hybrid
5**Cost**: Licensing and operational costs
Database Comparison
Database
Best For
Deployment
Latency
Pinecone
Managed cloud
Cloud
Low
Weaviate
Hybrid search
Both
Medium
Milvus
Large scale
Self-hosted
Low
pgvector
PostgreSQL users
Both
Medium
Qdrant
Open source
Both
Low
On-Premise Recommendation
For sovereign deployments, we recommend:
Primary: Milvus or Qdrant for dedicated vector search
Alternative: pgvector for PostgreSQL integration
Hybrid: Elasticsearch with vector capabilities
Retrieval Optimization
Query Enhancement
Improve retrieval by enhancing queries:
Query expansion with synonyms
Hypothetical document embedding (HyDE)
Query decomposition for complex questions
Hybrid Search
Combine vector and keyword search:
Dense retrieval for semantic similarity
Sparse retrieval (BM25) for exact matches
Score fusion for combined ranking
Re-ranking
Apply re-ranking for better results:
Cross-encoder re-ranking
LLM-based relevance scoring
Diversity optimization
LLM Integration Patterns
Context Window Management
Efficiently use the LLM context window:
Prioritize most relevant chunks
Compress context when needed
Use map-reduce for long documents
Prompt Structure
Structure prompts for optimal results (system, context, instructions, user query) and enforce:
Answer only from context
Explicit “not in context” handling
Citing specific sections where possible
Production Considerations
Monitoring and Observability
Track key metrics:
Retrieval precision and recall
Response latency
User satisfaction
Cost per query
Caching Strategies
Implement caching for efficiency:
Query cache for repeated questions
Embedding cache for documents
Response cache with TTL
Security
Ensure secure RAG deployment:
Access control for documents
Query filtering based on permissions
Audit logging
PII detection and handling
Conclusion
Building production-grade RAG systems requires careful attention to each component of the pipeline. By following these best practices, enterprises can deploy RAG systems that deliver accurate, relevant, and secure responses at scale.