Building a Vector Index in Azure AI Search: HNSW, Profiles, and RAG Retrieval
In this article, we will understand how vector search works in Azure AI Search and how to use it as the retrieval layer in a Retrieval-Augmented Generation (RAG...
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In this article, we will understand how vector search works in Azure AI Search and how to use it as the retrieval layer in a Retrieval-Augmented Generation (RAG...
The transition from large language models (LLMs) as simple chat interfaces to autonomous AI agents represents the most significant shift in enterprise software ...
The traditional paradigm of backend engineering has long been rooted in deterministic logic: "If X, then Y." However, as we integrate Large Language Models (LLMs) and specialized ML agents into produc...
In the early days of software engineering, a simple cron job on a single server was often sufficient to handle recurring tasks like database backups or report generation. However, as organizations tra...
Amazon Aurora is often marketed as the "silver bullet" for relational database scaling. By decoupling compute from storage and utilizing a log-structured distributed storage system, it solves many of ...
In the era of hyper-scale applications, the dream of a "global database" that is simultaneously fast, always available, and perfectly consistent everywhere is the holy grail of engineering. However, a...
In the modern distributed landscape, data is no longer a static asset sitting in a relational database; it is a continuous stream of pulses representing user behavior, system health, and financial tra...