The AI Search Landscape: From Vector Search to Agentic Search

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The AI Search Landscape: From Vector Search to Agentic Search

AI search is evolving rapidly. Traditional search engines are adding vector capabilities, while vector databases are expanding into keyword search, filtering, and hybrid retrieval.

But while many platforms now offer similar features, their underlying architectures can differ significantly.

Join AlphaSignal and Vespa ai for a technical exploration of the evolving AI search landscape and what it means for developers building modern AI applications.

Using a real-world AI shopping application, we'll examine how search requirements evolve from basic vector similarity to sophisticated retrieval systems that combine multimodal data, business signals, machine-learned ranking, and agent-driven search strategies.

Take your RAG pipeline beyond reranking

Explore practical guidance for combining retrieval signals, improving recall, and applying multi-phase ranking to balance accuracy, latency, and cost.

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