The rapid advancement of AI technology is undeniably reshaping human-machine interactions. Historically, communication with machines necessitated intermediaries like programming languages and structured scripts. Now, with the arrival of GPTs and AI-assisted bots, machine-learning models are approaching a deeper comprehension of natural human language. This evolution brings humans and machines closer, changing how we seek and find information.
From keyword-based searches to discovery-driven inquiries, people’s approach to search evolves: we are moving from searching for known items to exploring unknown territories, searching and finding concepts instead of focusing solely on words. Take a streaming media company: the searches across their platform might include the inquiry for a specific movie title, such as "Back to the Future," and conceptually driven searches like "Give me recommendations for a feel-good sci-fi movie from the 80s".
Consider everyday scenarios: spotting an item on the street and using an image to search for it or conducting voice searches instead of typing in words on keyboards. These are manifestations of our changing search behaviors. While traditional search methods excel in keyword-based queries, they falter with complex, nuanced requests.
A hybrid search approach integrates models from AI giants like OpenAI, enabling users to create and fine-tune vector embeddings, tailoring their search engine to specific business needs. Moreover, evolving AI models learn from user interactions, continuously improving search precision. The AI-enhanced layers bring a deeper semantic understanding, significantly improving result quality.
Click here to read the original article published by Meilisearch.
Please give your feedback on this article or share a similar story for publishing by clicking here.
