AI Search uses large language models to understand queries, retrieve semantically relevant results through vector search, and generate direct, coherent answers instead of just link lists. It improves context awareness, supports multi-turn conversations, synthesizes information from multiple sources, and is reshaping SEO practices to focus on clear structure, human-like content, and machine-readable formatting.
Word embeddings map words to numerical vectors so similar words have similar representations, making it easier for machine learning models to process language. Word2Vec builds these embeddings using neural networks with CBOW and skip-gram methods, learning from context to place related words closer in vector space while optimizing efficiently with techniques like negative sampling.
A vector database stores and indexes vector embeddings—numerical representations of data—so they can be quickly retrieved for similarity or semantic searches. By combining embeddings generated by machine learning models with efficient indexing structures, it enables use cases like semantic text search, image/audio/video similarity, recommendations, and giving large language models long-term memory.
Reranking is a second step in a search or retrieval pipeline where the top results from an initial search (e.g., vector search) are re-scored and reordered to improve relevance.
This often uses a more precise but slower model, such as a cross-encoder or an LLM, to focus on a smaller set of candidates.
It doesn’t retrain the model — it simply reorders the existing results so the most relevant appear first.