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05. Retrieval Augmented Generation

Authors
Affiliations
Birmingham City University
Sunway College Kathmandu

Objective

Combines information retrieval with generation to ground LLM outputs in external knowledge bases, improving accuracy and reducing hallucination.

System Architecture

[Mermaid diagram - flowchart showing core components and data flow]

[3-5 sentence description of architecture]

Technical Approach

Key Components

Pipeline / Data Flow

[Detailed description of request → processing → response flow]

Complexity Analysis

MetricComplexityNotes
Model sizeRetriever: 100M-1B, LLM: 7B-70B[implications]
Time complexityO(n) retrieval + O(seq_len²) generation[notes]
Space complexityVector index: 10GB-1TB, LLM: 14-140GB[notes]
Latency targetp95 <2s (retrieval + generation)[real-time vs. batch]
Throughput target10-50 req/s per GPU[per GPU/instance]

Pros & Cons

Pros

Cons

Trade-offs

[1-2 paragraphs discussing key technical trade-offs]

Real-World Applications

Where This Pattern Appears

Production Considerations

[2-3 paragraphs on scaling, failure modes, monitoring, cost]

References & Citations

Citation 1: Architecture & Design

Title: [Paper/Blog Title on Retrieval Augmented Generation Architecture]

Citation 2: Performance & Benchmarks

Title: [Performance Benchmarks for Retrieval Augmented Generation]

Citation 3: Implementation Details

Title: [Implementation Details and Trade-offs]

Citation 4: Real-World Deployment

Title: [Production Deployment Insights]

Reproducibility Checklist