Vespa.ai: Generative AI needs more than a Vector Database
Ensuring the reliability and effectiveness of AI systems remains a significant challenge. Generative AI must be combined with access to your company data in most use cases, a process called retrieval-augmented generation (RAG). The results from GenerativeAI are vastly improved when the model is enhanced with contextual data from your organization.
Most practitioners rely on vector embeddings to surface content based on semantic similarity. While this can be a great step forward, achieving good quality requires a combination of multiple vectors with text and structured data, using machine learning to make final decisions.
Vespa.ai, a leading player in the field, enables solutions that do this while keeping latencies suitable for end users, at any scale.
In this episode of the EM360 Podcast, Kevin Petrie, VP of research at BARC US speaks to Jon Bratseth, CEO of Vespa.ai, to discuss:
- the opportunity for Generative AI in business
- why you need more than vectors to achieve high quality in real systems
- how to create high-quality GenerativeAI solutions at an enterprise scale
