Author(s): Zoumana Keita

Originally published on Towards AI.

How search progressed from exact-term matching to systems that decide how to gather evidence.

A search box can create the impression that a computer understands a question. For much of the history of information retrieval, that impression was misleading. A system could locate the words a person typed, rank the documents containing them, and return a long list without fully understanding what the person actually meant. The familiar experience of receiving many results but no useful answer begins with this gap between matching language and understanding intent.

After introducing the motivation, the article walks through how retrieval evolved—from inverted indices with TF-IDF/BM25 that rank by lexical relevance, to semantic search using vector embeddings that recover meaning across different phrasings, and then hybrid retrieval that combines exact identifiers with conceptual recall. It explains how LLMs brought fluent generation but are limited by training data, leading to traditional RAG systems that fetch external passages at query time to ground answers. The author then covers improvements like query rewriting, retrieval reranking, and more accurate pipelines, before introducing agentic RAG, where an agent decides dynamically whether to search, which sources to consult, how to verify evidence, and when it has enough information to answer—enabling multi-step investigation and synthesis. Finally, it notes emerging vulnerabilities from this added autonomy and concludes that the main trend isn’t just better writing, but better information-seeking decisions: where to search, what to trust, and how to ensure evidence is sufficient.

Read the full blog for free on Medium.

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Published via Towards AI