15 articles · Page 1 of 2
What Is Hybrid Search?
Hybrid search combines keyword (BM25) and semantic vector retrieval, fusing both rankings to deliver higher recall and precision than either method alone.
What Is Passage Retrieval?
Passage retrieval extracts the most relevant paragraph from a page to answer a query. Learn how it powers AI Overviews, citations, and AEO.
Agent Knowledge Base Specification: Structure, Refresh, and Versioning
Production specification for AI agent knowledge bases: document model, chunking strategies, metadata enrichment, refresh cadence, version pinning, and rollback.
Agent Memory Pattern Specification: Short-Term, Long-Term, and Episodic
Specification for AI agent memory: working, episodic, semantic, and procedural tiers with consolidation, eviction, and PII handling.
Agent Vector Store Integration Specification: Pinecone, Weaviate, and pgvector
Production specification for agent vector store integration covering Pinecone, Weaviate, pgvector, indexing, hybrid search, and consistency.
Programmatic GEO: When to Scale Content with Templates (and Governance)
A framework for programmatic GEO: when templated content earns AI citations, what governance prevents thin output, and how to QA at scale across ChatGPT, Perplexity, and Google AI Overviews.
Vector Embedding Optimization Specification for GEO: Writing Content That Survives Semantic Retrieval
Writer-facing specification for vector embedding optimization: chunkable structures, anchor sentences, and metadata enrichment that survive RAG retrieval and dense vector search.
What is Chunking for RAG
Chunking for RAG explained: how splitting documents into retrievable units shapes citation accuracy across fixed-size, recursive, semantic, and sentence-window strategies.
What is Context Window Engineering
Context window engineering is the discipline of curating, ordering, and budgeting tokens in an LLM's context to maximize accuracy and minimize hallucinations.
What Is Knowledge Graph Grounding
Knowledge graph grounding ties LLM answers to structured entities and relations from sources like Google Knowledge Graph and Wikidata so facts can be verified, disambiguated, and cited.
What is Query Fan-Out in AI Search
Query fan-out is how AI search engines decompose a single question into many parallel sub-queries to retrieve diverse sources and synthesize a grounded answer.
What Is RAG (Retrieval-Augmented Generation)
RAG (retrieval-augmented generation) pairs a retriever and an LLM so answers are grounded in fresh, citable sources rather than the model's parametric memory alone.