Voyage AI is building RAG tools to make AI hallucinate less
A startup called Voyage AI is building tools, including custom AI models, that serve as the foundation of RAG generative AI systems.
AI tends to make things up. That’s unappealing to just about anyone who uses it on a regular basis, but especially to businesses, for which fallacious results could hurt the bottom line. Half of workers responding to a recent survey from Salesforce say they worry answers from their company’s generative AI-powered systems are inaccurate.
Enhancing AI Accuracy with Voyage AI's Retrieval-Augmented Generation Technology: A Game-Changer in the AI Landscape
In the rapidly evolving landscape of artificial intelligence (AI), the quest for more reliable and accurate AI systems has become a tOP priority. One technique that has gained significant attention in recent years is Retrieval-Augmented Generation (RAG), which pairs an AI model with a knowledge base to provide supplemental information before answering, serving as a fact-checking mechanism.
Voyage AI, a company founded by Stanford professor Tengyu Ma in 2023, has built its business around RAG, providing solutions for companies such as Harvey, Vanta, Replit, and SK Telecom.
The Problem of Hallucinations in AI: A Major Concern
While RAG can help mitigate the issue of hallucinations in AI, no technique can completely eliminate them. Hallucinations occur when an AI model generates responses that are not supported by the input data or context. This can lead to inaccurate or misleading results, which can have serious consequences in industries such as finance, healthcare, and law. For instance, a hallucination in a medical diagnosis AI system could lead to a misdiagnosis, resulting in incorrect treatment and potentially life-threatening consequences.
Voyage AI's Approach to RAG: A Novel Solution
Voyage AI's approach to RAG involves training AI models to convert text, documents, PDFs, and other forms of data into numerical representations called vector embeddings. These embeddings capture the meaning and relationships between different data points in a compact format, making them useful for search-related applications like RAG. Voyage AI uses a particular type of embedding called contextual embedding, which captures not only the semantic meaning of data but also the context in which the data appears. For example, given the word "bank" in the sentences "I sat on the bank of the river" and "I deposited money in the bank," Voyage's embedding models would generate different vectors for each instance of "bank" – reflecting the different meanings implied by the context.
Benefits of Voyage AI's RAG Technology: A Cost-Effective and Customizable Solution
Voyage AI's RAG technology offers several benefits, including:
Voyage AI's Growth and Funding: A Bright Future Ahead
Voyage AI has just over 250 customers and has raised a total of $28 million in funding, including a $20 million Series A round led by CRV with participation from Wing VC, Conviction, Snowflake, and Databricks. The company plans to use the funding to launch new embedding models and double its size. With its innovative approach to RAG and its commitment to providing high-quality solutions, Voyage AI is well-positioned to become a leader in the RAG space.
Conclusion
Voyage AI's Retrieval-Augmented Generation technology has the potential to revolutionize the way companies approach AI-powered search and retrieval. By providing a more accurate and context-aware approach to RAG, Voyage AI is helping companies to improve the quality of their AI-powered responses and reduce the risk of hallucinations. With its customizable solutions and cost-effective approach, Voyage AI is poised to become a major player in the AI landscape, helping companies to make better decisions and drive business success.
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