Large Scale Distributed Retrieval-Augmented Generation
Chapter 1: Foundations of Scalable RAG Architectures
Review of RAG Core Components
Identifying Bottlenecks and Limitations in Scaling RAG
Principles of Distributed Systems Applied to RAG
Architectural Patterns for Distributed RAG Systems
Metrics for Evaluating Large-Scale RAG Systems
Designing for High Availability and Fault Tolerance
Data Consistency Models in Distributed RAG
Chapter 2: Advanced Distributed Retrieval Strategies
Scaling Vector Search: Sharding Replication and Indexing
Distributed Dense Retrieval: Implementations and Optimizations
Hybrid Search at Scale: Combining Dense and Sparse Retrievers
Graph-Based Retrieval in Distributed Environments
Multi-Vector and ColBERT-style Architectures for Scale
Advanced Re-ranking Pipelines in Distributed Settings
Near Real-Time Indexing for Large-Scale Data Ingestion
Hands-on Practical: Implementing a Sharded Vector Index
Chapter 3: Optimizing Large Language Models for Distributed RAG
Efficient LLM Serving Architectures
Parameter-Efficient Fine-Tuning for Domain-Specific RAG
Quantization and Pruning Techniques for LLM Deployment
Managing Long Contexts with Large Retrieved Datasets
Strategies for Mitigating Hallucinations at Scale
Multi-LLM RAG Architectures and Intelligent Routing
Hands-on Practical: Fine-tuning an LLM for Task-Specific RAG
Chapter 4: Data Ingestion and Processing Pipelines at Scale
Distributed Data Ingestion Frameworks
Scalable Document Chunking and Preprocessing Strategies
Distributed Embedding Generation and Management
Change Data Capture for Real-time RAG Updates
Vector Database Management and Optimization at Scale
Data Governance and Lineage in Distributed RAG Systems
Hands-on Practical: Building a Scalable Data Ingestion Pipeline
Chapter 5: Orchestration and Operationalization of Large-Scale RAG
Workflow Orchestration with Airflow or Kubeflow
Microservice Design Patterns for RAG Components
Containerization and Kubernetes for RAG Deployment
Advanced Monitoring Logging and Alerting for Distributed RAG
CI CD Pipelines for RAG Systems
A B Testing and Experimentation Frameworks for RAG
Cost Optimization Strategies for Cloud-Based RAG
Hands-on Practical: Deploying RAG on Kubernetes with Monitoring
Chapter 6: Advanced RAG Architectures and Techniques
Multi-Hop and Iterative RAG at Scale
Agentic RAG Systems with Distributed Tool Usage
Knowledge Graph-Augmented RAG in Distributed Settings
Self-Correcting and Self-Improving RAG Systems
Handling Highly Dynamic and Streaming Data Sources
Security Considerations in Large-Scale RAG Deployments
Cross-Lingual and Multimodal RAG at Scale
Practice: Designing a Multi-Stage RAG System
Chapter 7: Performance Tuning and Benchmarking for Distributed RAG
Identifying Performance Bottlenecks in RAG Components
Latency and Throughput Optimization Techniques
Load Balancing Strategies for RAG Components
Caching Mechanisms at Different System Layers
Benchmarking Distributed RAG: Metrics and Tools
Stress Testing and Capacity Planning for RAG
Performance Profiling and Debugging in Distributed Environments
Practice: Optimizing a Distributed RAG System for Peak Performance
Quantization and Pruning Techniques for LLM Deployment
New · Open Source
Kerb - LLM Development Toolkit
Python toolkit for building production-ready LLM applications. Modular utilities for prompts, RAG, agents, structured outputs, and multi-provider support.
Was this section helpful?
- The Lottery Ticket Hypothesis: Finding Sparse, Trainable Neural Networks, Jonathan Frankle, Michael Carbin, 2019 International Conference on Learning Representations (ICLR) DOI: 10.48550/arXiv.1803.03635 - Presents a foundational concept in neural network pruning, positing that sparse subnetworks can be found within larger networks that achieve comparable performance.
- Quantization for Deep Learning Models, PyTorch Documentation, 2019 (PyTorch Foundation) - Official documentation for PyTorch's quantization module, covering post-training quantization (PTQ) and quantization-aware training (QAT).
© 2025 ApX Machine LearningEngineered with