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
Implementing Guardrails and Content Safety
Was this section helpful?
- Constitutional AI: Harmlessness from AI Feedback, Yuntao Bai, Saurav Kadavath, Sandipan Kundu, Amanda Askell, Jackson Kernion, Andy Jones, Anna Chen, Anna Goldie, Azalia Mirhoseini, et al., 2022 arXiv preprint DOI: 10.48550/arXiv.2212.08073 - Discusses methods for aligning LLMs with human values and safety principles using AI feedback, which is a method for training LLMs to moderate their own behavior without extensive human intervention, relevant to the LLM-as-a-judge technique.
- Holistic Evaluation of Language Models, Percy Liang, Rishi Bommasani, Tony Lee, Dimitris Tsipras, Dilara Soylu, Michihiro Yasunaga, Yian Zhang, Deepak Narayanan, Yuhuai Wu, Ananya Kumar, Benjamin Newman, Binhang Yuan, Bobby Yan, Ce Zhang, Christian Cosgrove, Christopher D. Manning, Christopher RĂ©, Diana Acosta-Navas, Drew F. Hudson, Eric Zelikman, Esin Durmus, Faisal Ladhak, Frieda Rong, Hongyu Ren, Huaxiu Yao, Jue Wang, Keshav Santhanam, Laurel Orr, Lucia Zheng, Mert Yuksekgonul, Mirac Suzgun, Nathan Kim, Neel Guha, Niladri Chatterji, Omar Khattab, Peter Henderson, Qian Huang, Ryan Chi, Sang Michael Xie, Shibani Santurkar, Surya Ganguli, Tatsunori Hashimoto, Thomas Icard, Tianyi Zhang, Vishrav Chaudhary, William Wang, Xuechen Li, Yifan Mai, Yuhui Zhang, Yuta Koreeda, 2023 Transactions on Machine Learning Research DOI: 10.48550/arXiv.2211.09110 - Provides a framework for evaluating LLMs across various criteria, including safety, fairness, and robustness, offering context for assessing the overall reliability and risks that guardrails address.
- Google Cloud Perspective API Documentation, Google Cloud, 2024 (Google Cloud) - Official documentation for a widely used content moderation API that employs machine learning models to detect various categories of harmful or toxic content, serving as an example of model-based classifiers for output guardrails.