Production LLMOps

Designing, Deploying, and Operating Large Language Model Systems at Scale

Introduction: The LLMOps Imperative

1. Why LLM Projects Fail in Production
2. Defining LLMOps: What It Is and What It Is Not
3. The Generative AI Lifecycle vs Traditional ML
4. Principles That Endure Beyond the Tooling
5. How to Use This Book

Chapter 1: Foundations of LLM Application Architecture

1. How Large Language Models Work: A Primer for Engineers
2. Architectural Patterns for LLM Applications
3. Requirements Engineering for Generative AI Systems
4. System Design Trade-offs and Decision Frameworks
5. From Proof of Concept to Production: The Gap Most Teams Ignore

Chapter 2: Model Selection, Benchmarking, and the Proprietary vs Open-Weight Decision

1. The LLM Landscape: Categories and Capabilities
2. Benchmarking Methodologies: What Metrics Actually Matter
3. Designing Your Own Evaluation Benchmarks
4. Proprietary API Models: When They Make Sense
5. Open-Weight Models: Self-Hosting, Licensing, and Control
6. Multi-Model Strategies and Model Portfolios

Chapter 3: Prompt Engineering, Structured Outputs, and Tool Use

1. Prompt Design from First Principles
2. Advanced Prompting Techniques: When Each One Applies
3. Structured Outputs: JSON Schemas and Validation
4. Function Calling and Tool Use Patterns
5. Prompt Management as a Production Discipline
6. Anti-Patterns and Common Failures

Chapter 4: Embeddings, Vector Databases, and Retrieval Architectures

1. How Embeddings Work: From Text to Vectors
2. Vector Database Architectures and Selection Criteria
3. Semantic Search vs Keyword Search vs Hybrid Retrieval
4. Reranking: Precision at the Cost of Latency
5. Context Engineering and Management
6. Building a Production Retrieval Pipeline

Chapter 5: Retrieval-Augmented Generation and Advanced RAG Patterns

1. The RAG Architecture: Why Retrieval Before Generation
2. Basic RAG Implementation End to End
3. Advanced RAG Patterns: Multi-Hop, Graph-Based, Agentic
4. Document Processing Pipelines for Production RAG
5. Evaluating RAG Systems: Beyond Accuracy
6. When Not to Use RAG

Chapter 6: Agents, Memory, and Multi-Agent Systems

1. What Makes an Agent: Planning, Tools, and Execution Loops
2. Single-Agent Architectures and Implementation Patterns
3. Memory Systems: Short-Term, Long-Term, and Vector Memory
4. Multi-Agent Orchestration Patterns
5. Operational Challenges of Agentic Systems
6. When Agents Add Value—and When They Overcomplicate

Chapter 7: Fine-Tuning, Parameter-Efficient Methods, and Synthetic Data

1. When Fine-Tuning Beats Prompt Engineering
2. Dataset Preparation: Collection, Curation, and Versioning
3. Full Fine-Tuning vs Parameter-Efficient Fine-Tuning
4. Synthetic Data Generation Strategies
5. Training Infrastructure and Cost Considerations
6. Maintaining Tuned Models in Production

Chapter 8: Evaluation Frameworks, Experimentation, and LLM-as-a-Judge

1. The Evaluation Problem: Why Traditional Metrics Fail
2. Automated Quality Metrics for Generative AI
3. LLM-as-a-Judge: Techniques, Risks, and Best Practices
4. Human-in-the-Loop Evaluation Workflows
5. Experimentation Platforms and A/B Testing for LLMs
6. Building a Continuous Evaluation Culture

Chapter 9: Versioning, Registries, CI/CD/CT for LLM Applications

1. What Needs Versioning in an LLM System
2. Model Registries and Lifecycle Management
3. Prompt Versioning and Canary Testing
4. CI/CD for LLM Applications: Pipelines That Work
5. Continuous Training and Automated Retriggering
6. Rollout Strategies: Blue-Green, Canary, Shadow Deployments

Chapter 10: Deployment, Inference Serving, and Infrastructure

1. API Design for LLM Services
2. Containerizing LLM Applications and Models
3. Kubernetes Orchestration for LLM Workloads
4. GPU Infrastructure: Procurement, Scheduling, and Sharing
5. Cloud Deployment Patterns and Serverless Inference
6. Multi-Region Deployment and Latency Optimization

Chapter 11: Inference Optimization, Caching, and Model Routing

1. Quantization: Trading Precision for Speed and Cost
2. Batching Strategies: Static, Dynamic, and Continuous
3. Caching Patterns: Prompt Caching, Semantic Cache, Result Caching
4. Speculative Decoding and Early Exit Techniques
5. Model Gateways and Intelligent Routing
6. End-to-End Latency and Throughput Optimization

Chapter 12: Observability, Monitoring, and Reliability Engineering

1. Observability Foundations for LLM Systems
2. Distributed Tracing Across LLM Pipelines
3. Quality Monitoring: Hallucination Detection and Drift
4. Token Analytics, Cost Monitoring, and FinOps
5. SRE Practices: SLIs, SLOs, Error Budgets for LLMs
6. Incident Response and Disaster Recovery

Chapter 13: Security, Governance, Compliance, and Responsible AI

1. Prompt Injection Attacks and Defense Strategies
2. Data Leakage Prevention and Input/Output Sanitization
3. Secrets Management and Credential Security
4. Authentication, Authorization, and Access Control
5. Privacy, Regulations, and Compliance Requirements
6. Responsible AI Governance Frameworks

Conclusion: Operating LLM Systems at Scale

1. The LLMOps Maturity Model: From Experimentation to Platform
2. Team Structures and Organizational Patterns
3. Building an Internal LLMOps Platform
4. What Changes, What Endures
5. The Path Ahead

References