Context Engineering

Designing Intelligent Systems with Large Language Models

Introduction: The Context Problem

Chapter 1: Foundations: What Is Context Engineering?

1. From Prompts to Systems: The Evolution of LLM Interaction
2. Defining Context Engineering: Scope, Goals and Principles
3. The Three Layers of Context: Task, Domain and System
4. Why Naive Prompting Fails in Production
5. The Cost of Context: Tokens as a First-Class Resource

Chapter 2: The Context Window: Architecture and Mechanics

1. How Transformers Use Context: Attention and Positional Encoding
2. KV Caching and Its Implications for Latency
3. Sliding Windows, Long Context and Compression Strategies
4. Positional Overflow and Context Degradation
5. Choosing a Model Based on Context Requirements
6. Key Takeaways

Chapter 3: Prompt Construction as Context Design

1. Structural Patterns for Robust Prompts
2. Few-Shot Design: Selection, Ordering and Calibration
3. System Instructions vs. User Context
4. Prompt Templates as Code: Versioning, Testing, Composition
5. Common Anti-Patterns and How to Avoid Them

Chapter 4: Retrieval-Augmented Generation: Core Patterns

1. The RAG Pattern: Architecture and Variants
2. Document Chunking Strategies and Their Trade-offs
3. Query Transformation: Rewriting, Expansion and Decomposition
4. Re-ranking and Multi-stage Retrieval
5. Evaluating Retrieval Quality Independently of Generation
6. Advanced RAG Patterns: GraphRAG, Agentic Retrieval and Beyond
7. RAG Failure Modes and Diagnosis
8. Key Takeaways

Chapter 5: Vector Search and Semantic Indexing

1. Embeddings: Selection, Fine-Tuning and Dimensions
2. Vector Database Architectures: In-Memory vs. Persistent vs. Managed
3. Approximate Nearest Neighbor Algorithms Explained
4. Hybrid Search: Combining Semantic, Lexical and Metadata Filters
5. Index Maintenance, Refresh Strategies and Consistency

Chapter 6: Memory Systems and State Management

1. Short-Term vs. Long-Term Memory Patterns
2. Conversation History Management and Summarization
3. Persistent Knowledge: Entity Extraction and Knowledge Graphs
4. Memory in Multi-turn Agents
5. Privacy, Consent and Forgetting in Memory Systems

Chapter 7: Tool Use and the Model Context Protocol

1. Tool Calling: Patterns, Schemas and Error Handling
2. The Model Context Protocol: Architecture and Design Philosophy
3. Building MCP Servers: Tools, Resources and Prompts
4. Tool Discovery, Selection and Composition
5. Security Considerations for Tool Execution
6. Key Takeaways

Chapter 8: Agents, Planning and Reasoning Architectures

1. Agent Architectures: From ReAct to Advanced Patterns
2. Planning with Context: Decomposition and Subgoal Management
3. Reflection, Self-Correction and Iterative Improvement
4. Agent Reliability Patterns for Production Systems
5. Multi-Agent Systems: Coordination and Communication Protocols
6. When Agents Help and When They Hurt

Chapter 9: Structured Outputs and Controlled Generation

1. Schema-Driven Output Design
2. Constrained Decoding and Grammar-Based Generation
3. Validation Pipelines: Pre- and Post-Generation Checks
4. Structured Outputs in Streaming Contexts
5. Trade-offs: Flexibility vs. Reliability

Chapter 10: Orchestration and Workflow Engines

1. Workflow Patterns: Sequential, Parallel, Conditional, Loops
2. Orchestration Frameworks: LangGraph, Temporal and Alternatives
3. Error Handling, Retries and Circuit Breakers in LLM Workflows
4. State Management Across Workflow Steps
5. Designing Observable and Debuggable Workflows
6. Key Takeaways

Chapter 11: Performance Engineering: Caching, Optimization, Latency

1. Caching Strategies: Exact Match, Semantic, Prefix and Hybrid
2. Token Optimization: Compression, Pruning and Rewriting
3. Batching, Parallelization and Throughput Optimization
4. Speculative Decoding and Draft Models
5. Latency Budgeting and Performance SLOs

Chapter 12: Evaluation, Testing and Observability

1. Offline Evaluation: Datasets, Metrics and LLM-as-Judge
2. Online Evaluation: A/B Testing and Canary Deployments
3. Regression Testing for Prompts, Retrieval and Workflows
4. Observability: Tracing, Logging, Metrics and Dashboards
5. Building a Continuous Evaluation Pipeline
6. Key Takeaways

Chapter 13: Reliability and Safety: Hallucination Mitigation, Security, Governance

1. Hallucination: Causes, Detection and Mitigation Strategies
2. Prompt Injection and Indirect Attacks
3. Data Privacy, PII Handling and Confidentiality
4. Security for Tool Use and External Integrations
5. Governance: Policies, Guardrails and Compliance
6. Advanced Indirect Injection Variants
7. MCP Security Hardening Checklist
8. Key Takeaways

Chapter 14: Production Deployment at Scale

1. Cloud-Native Deployment Architectures
2. Scaling Patterns: Horizontal, Vertical and Hierarchical
3. Multi-Region and Edge Considerations
4. Cost Management and FinOps for LLM Systems
5. Incident Response and Post-Mortem Patterns
6. Key Takeaways

Chapter 15: Case Studies in Context Engineering

1. Case Study 1: Optimizing Latency for a Fintech RAG System
2. Case Study 2: Migrating from Monolithic Prompts to MCP-Based Agents
3. Case Study 3: Building a Multilingual Customer Support System
4. Case Study 4: Implementing Structured Outputs for a Legal Document Analysis Pipeline
5. Case Study 5: Scaling a Research Agent System with Evaluation-Driven Iteration

Chapter 16: Frontiers of Context Engineering

1. Scaling Laws for Long Context and Their Limits
2. Multimodal Context: Vision, Audio and Beyond
3. In-Context Learning Theory: What We Know and Do Not Know
4. Neuro-Symbolic Integration and Structured Reasoning
5. The Next Three Years: Trends to Watch

Conclusion: The Discipline of Context

References

Glossary

Index

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