Core Technologies and Architecture
Modern AI search engines rely on sophisticated neural architectures and semantic understanding technologies to deliver intelligent results. This category examines the foundational models, algorithms, and systems that power semantic search, from vector embeddings to language models. Explore how these interconnected technologies work together to transform queries into meaningful, contextually relevant answers.
Embedding Models and Similarity Matching
Transform text into numerical vectors to measure semantic similarity between queries and documents.
Knowledge Graphs and Entity Recognition
Structure information as interconnected entities to enhance search context and relationship understanding.
Large Language Models and Transformers
Leverage deep learning architectures that understand and generate human-like text at scale.
Natural Language Processing and Understanding
Process and interpret human language to extract meaning, intent, and context from queries.
Neural Ranking and Re-ranking Systems
Apply machine learning models to score and prioritize search results for relevance.
Retrieval-Augmented Generation (RAG)
Combine document retrieval with generative AI to produce accurate, grounded search responses.
Vector Databases and Semantic Search
Store and query high-dimensional embeddings for fast, meaning-based information retrieval.