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Semantic Search

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AWS Vector Databases Explained: Semantic Search and RAG Systems

2025-12-04T07:08:08+00:00

The generative AI (GenAI) revolution has transformed how organizations extract value from their data. While large language models (LLMs) demonstrate remarkable capabilities in understanding and generating human-like text, their true enterprise potential is unlocked only when they can access proprietary, domain-specific information. This necessity has propelled vector databases from a specialized niche into an essential pillar of modern AI infrastructure. But First, What Are Vector Databases? A vector database, as its name suggests, is a type of database designed to store, index, and efficiently search vector embeddings. These vectors are high-dimensional points that represent meaning.  At its core, a vector [...]

AWS Vector Databases Explained: Semantic Search and RAG Systems2025-12-04T07:08:08+00:00

Mastering Cloud-Based Semantic Search: Advanced Cloud Search Architectures Made Easy

2025-10-28T18:05:37+00:00

In today's AI-driven world, finding information based on meaning and context rather than exact keywords has become crucial. Good thing we now have vector search, enabling semantic search, personalized recommendations, image similarity, and more. However, building and managing vector search infrastructure can be complex. Fortunately, AWS offers zero-infrastructure managed services that let you implement powerful vector search capabilities without worrying about servers, scaling, or maintenance. Let's walk through creating a simple yet effective vector search demo using AWS services within the Free Tier so you can follow along without incurring costs. What is Vector Search? Vector search is a groundbreaking [...]

Mastering Cloud-Based Semantic Search: Advanced Cloud Search Architectures Made Easy2025-10-28T18:05:37+00:00

What is Retrieval Augmented Generation (RAG) in Machine Learning?

2025-06-30T03:46:57+00:00

Retrieval-Augmented Generation (RAG) Cheat Sheet Retrieval-Augmented Generation (RAG) is a method that enhances large language models (LLMs) outputs by incorporating information from external, authoritative knowledge sources. Instead of relying solely on pre-trained data, RAG retrieves relevant content at inference time to ground its responses. LLMs (Large Language Models) are trained on massive datasets and use billions of parameters to perform tasks like: Question answering Language translation Text completion RAG extends LLM capabilities to domain-specific or private organizational knowledge without requiring model retraining. It provides a cost-efficient way to improve the relevance, accuracy, and utility of LLM outputs in dynamic or [...]

What is Retrieval Augmented Generation (RAG) in Machine Learning?2025-06-30T03:46:57+00:00

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