Vector Databases for Enterprise AI by Emma McGrattan (.ePUB)+
File Size: 10 MB
Vector Databases for Enterprise AI: Semantic Retrieval Systems for RAG, Search, and AI Applications by Emma McGrattan
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Overview: Enterprise Generative AI has reached a turning point. Pilots have proven the models work. What’s struggling is the infrastructure underneath them. Semantic search, RAG, and agentic workflows require data retrieval based on meaning and similarity, not keywords and exact matches. Better models won’t fix an architecture that was never designed for this kind of reasoning. Enterprise data platforms are shaped by who and what consumes data. For most of their history, that consumer was human. Analysts wrote SQL queries, applications executed deterministic transactions, and dashboards reflected predefined metrics. The systems we built, including databases, search engines, and pipelines, were optimized for precision, predictability, and structure. Those assumptions held for decades, and they still matter today. The adoption of large language models (LLMs) and AI-driven applications introduces a different kind of consumer. Instead of asking precise questions, these systems retrieve information probabilistically and reason over relevance rather than correctness. Techniques such as retrieval-augmented generation (RAG) and semantic search depend on similarity-based retrieval across both structured and unstructured data. This shift does not make traditional databases obsolete, but it does expose clear limits to how they support semantic access to information. Vector databases are an architectural response to this shift. By storing and retrieving embeddings (numerical representations that capture the meaning of text, images, or other data), vector databases enable AI systems to find relevant data without relying solely on schema or exact matches.
Genre: Non-Fiction > Tech & Devices
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