What are Retrieval-based Models?
AI models that rely on retrieving relevant information, often using techniques like sparse retrieval or dense retrieval, rather than generating responses from scratch.
More about Retrieval-based Models:
Retrieval-based Models are AI systems designed to fetch and present the most relevant information from a predefined dataset or knowledge base. These models often utilize sparse retrieval methods for term-based matching or dense retrieval to capture semantic relationships.
Retrieval-based models are widely used in semantic search, question answering, and recommendation systems, where accuracy and relevance are crucial.
Frequently Asked Questions
What is the main advantage of retrieval-based models?
They provide highly accurate and fact-based responses by retrieving pre-existing information from reliable sources, such as vector databases.
How do retrieval-based models differ from generative models?
Retrieval-based models fetch existing data, while generative models create new text using approaches like RAG.
From the blog
How SiteSpeakAI's YouTube Summarizer Can Transform Your Content Creation Strategy
Discover how SiteSpeakAI's YouTube Summarizer can revolutionize your content strategy. Learn to transform YouTube videos into SEO-optimized articles for your blog or website in under a minute. Boost engagement and search rankings effortlessly. Explore now.
Herman Schutte
Founder
How to Get Your Small Business Ready for AI
You keep hearing about Artificial Intelligence (AI) and wonder what it’s got to do with your business. The buzz is strong and it definitely sounds exciting, but is this big, must-go party exclusively for multibillion-dollar companies, or can small businesses get an invite, too?
Ane Guzman
Contributor