Optimizing RAG Systems for Production Environments
Chapter 1: Foundations of Production-Grade RAG Systems
Production RAG Architecture: Scaling Considerations
Identifying Performance Bottlenecks in RAG Pipelines
Advanced Metrics for Production RAG Evaluation
Long-Term Maintenance Challenges for RAG Systems
Production Infrastructure Considerations for RAG
Version Control and Experiment Tracking for RAG Components
Security Considerations in Production RAG
Chapter 2: Advanced Retrieval Optimization Techniques
Domain-Specific Fine-tuning of Embedding Models
Hybrid Search: Combining Dense and Sparse Retrievers
Advanced Re-ranking Architectures for Relevance
Query Augmentation: Expansion and Transformation
Optimizing Chunking Strategies for Diverse Data Sources
Advanced Document Representations: Multi-vector and ColBERT
Integrating Knowledge Graphs for Enhanced Retrieval
Active Learning for Retriever Improvement
Hands-on: Implementing and Evaluating Advanced Re-ranking
Chapter 3: Optimizing the Generation Component
Fine-tuning LLMs for RAG-Specific Generation Tasks
Controlling LLM Output: Style, Tone, and Factuality
Mitigating Hallucinations in RAG Outputs
Advanced Prompt Engineering for Production RAG
Efficient LLMs: Distillation and Quantization
Implementing Guardrails and Content Safety
Production Evaluation of Generated Content Quality
Hands-on: Fine-tuning a Smaller LLM for a RAG Task
Chapter 4: End-to-End RAG System Performance Optimization
Analyzing and Reducing RAG System Latency
Scaling RAG Throughput for Peak Loads
Implementing Caching Strategies in RAG Pipelines
Asynchronous Processing and Request Batching
Vector Database Optimization: Indexing and Sharding
Utilizing Hardware Acceleration for RAG
Load Balancing and Autoscaling Production RAG
Hands-on: Profiling and Optimizing a RAG Pipeline for Latency
Chapter 5: Cost Optimization for Production RAG
Identifying Cost Drivers in Production RAG
Cost-Effective Model Selection for RAG
Techniques for Minimizing LLM Token Usage
Optimizing Data Ingestion and Storage Costs
Choosing Infrastructure: Serverless vs. Provisioned for RAG
Implementing Usage Quotas and Budgets
Monitoring and Alerting for Cost Anomalies
Practice: Cost Modeling for a Sample RAG Application
Chapter 6: Advanced Evaluation and Monitoring in Production
Advanced RAG Evaluation Frameworks (RAGAS, ARES)
Offline vs. Online Evaluation Strategies
Automated Evaluation Pipelines
Monitoring Drift in Retrieval Components
Monitoring LLM Performance in RAG Systems
Integrating User Feedback for RAG Refinement
A/B Testing Strategies for RAG Optimization
Building RAG System Health Dashboards
Hands-on: Implementing a RAG Monitoring Dashboard
Chapter 7: Scalability, Reliability, and Maintainability
Architecting Highly Available RAG Systems
Implementing Fault Tolerance in RAG
Managing Knowledge Base Updates and Refresh Cycles
Multi-Tenancy and Managing Multiple RAG Instances
Automating RAG Deployments with CI/CD Pipelines
Data Governance and Lineage in RAG Systems
Advanced Debugging of Production RAG Issues
Operational Documentation for RAG Systems
Practice: Designing a Scalable RAG Architecture
A/B Testing Strategies for RAG Optimization
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.
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- Trustworthy Online Controlled Experiments: A Practical Guide to A/B Testing, Ron Kohavi, Diane Tang, Ya Xu, 2020 (Cambridge University Press) DOI: 10.1017/9781108653985 - A comprehensive guide on designing, implementing, and analyzing A/B tests in online environments, covering statistical rigor and common challenges.
- Overlapping Experiment Infrastructure: More, Better, Faster Experimentation, Diane Tang, Ashish Agarwal, Deirdre O'Brien, Mike Meyer, 2010 Proceedings of the 16th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (ACM) DOI: 10.1145/1835804.1835808 - Describes infrastructure and methodological considerations for efficiently running multiple concurrent A/B tests in a large-scale production setting.