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Chapter 11: Retrieval for Contextual AI

Understand how search provides grounding context for large language models and other AI systems.

Chapter Overview​

Retrieval-augmented generation (RAG) has become a foundational design pattern for AI applications. This chapter covers how search systems provide the context layer that grounds LLMs in facts, from embedding stores to context window optimization.

Understanding retrieval for contextual AI is critical as over 60% of enterprises deploying LLMs now integrate vector-based or hybrid search.

11.1 Context and Retrieval-Augmented Generation (RAG)​

11.1.1 Context windows and model context protocols

11.1.2 How RAG works

11.2 Embedding Stores​

11.2.1 Chunking and splitting

11.2.2 Vector ingestion

11.2.3 Refresh and expiry

11.3 Performance at Scale​

11.3.1 Metadata + vector binding

11.3.2 Search latency vs. LLM latency

11.3.3 Pipeline optimization

Examples​

Examples coming soon.

Code examples for this chapter will demonstrate RAG pipeline construction, embedding management, and performance optimization with Lucenia and LLM integrations.