RAG Architectures

Concepts, patterns, and practical guidance on RAG Architectures within Data, Retrieval, and Knowledge.

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Citation Grounding and Faithfulness Metrics
Citation Grounding and Faithfulness Metrics Citations are how an AI system shows its work. They are not decoration and they are not a marketing feature. They are an engineering mechanism that constrains what the system is allowed to claim. When a system cites well, users can verify important points quickly, operators can diagnose failures, and […]
Knowledge Graphs: Where They Help and Where They Don’t
Knowledge Graphs: Where They Help and Where They Don’t Knowledge graphs are one of the most misunderstood tools in modern AI systems. Some teams expect a graph to replace retrieval, replace reasoning, or magically eliminate mistaken answers. Other teams dismiss graphs as expensive toys that never keep up with changing content. Both instincts can be […]
Long-Form Synthesis from Multiple Sources
Long-Form Synthesis from Multiple Sources There is a difference between collecting information and producing understanding. Retrieval systems make collection cheap. Synthesis is the step that turns a pile of passages into a coherent answer that survives scrutiny. Long-form synthesis is not a decorative capability. It is an operational requirement whenever users ask questions that cannot […]
Query Rewriting and Retrieval Augmentation Patterns
Query Rewriting and Retrieval Augmentation Patterns A retrieval system is a translator between human intent and an index. People ask for “the thing I mean,” not “the token sequence that matches your data store.” Query rewriting exists because natural language is flexible and indexes are literal. The goal is not to rewrite for its own […]
RAG Architectures: Simple, Multi-Hop, Graph-Assisted
RAG Architectures: Simple, Multi-Hop, Graph-Assisted Retrieval-augmented generation is a system pattern: generate answers with evidence that the system retrieves. The most important word is “system.” Success depends less on any single model and more on how retrieval, ranking, context construction, and answer synthesis cooperate under real constraints. When this cooperation is weak, the model fills […]
Reranking and Citation Selection Logic
Reranking and Citation Selection Logic Retrieval systems succeed or fail in the space between “candidate generation” and “final evidence.” Candidate generation is designed to be fast and broad. It prefers recall, often returning passages that are merely related, not necessarily decisive. Reranking is the step that restores precision. It is the stage where the system […]

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