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
Advanced RAG Evaluation Frameworks (RAGAS, ARES)
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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- RAGAS: Automated Evaluation of Retrieval Augmented Generation, Shahul Es, Jithin James, Luis Espinosa-Anke, Steven Schockaert, 2023 arXiv preprint arXiv:2309.15217 DOI: 10.48550/arXiv.2309.15217 - Introduces the RAGAS framework and its component-wise metrics for evaluating RAG systems using LLMs as judges.
- Retrieval-Augmented Generation for Large Language Models: A Survey, Yunfan Gao, Yun Xiong, Xinyu Gao, Kangxiang Jia, Jinliu Pan, Yuxi Bi, Yi Dai, Jiawei Sun, Meng Wang, Haofen Wang, 2023 arXiv preprint arXiv:2312.10997 DOI: 10.48550/arXiv.2312.10997 - Provides a comprehensive overview of retrieval-augmented generation, including discussions on evaluation methodologies and challenges in real-world scenarios.