Building high-quality RAG applications with Qdrant and Quotient
Building high-quality RAG applications with Qdrant and Quotient
Building high-quality RAG applications with Qdrant and Quotient
Building high-quality RAG applications with Qdrant and Quotient
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We're excited to announce a new, dynamic partnership between Quotient and Qdrant.
This collaboration marks a significant step forward in AI development by offering developers a comprehensive, end-to-end solution for building, evaluating, and shipping Large Language Model (LLM) products using Retrieval-Augmented Generation (RAG). By combining Quotient's state-of-the-art generative AI evaluation technology with Qdrant's cutting-edge vector search, AI developers can gain the tools they need to bring best-in-class AI products to market.
Quotient's solutions play a crucial role in bridging the gap between intention and outcome in LLM product development. By offering fast, real-world, data-backed experimentation, we empower developers to define, test, and improve products that meet the unique requirements of their users.
Domain-specific evaluation is essential to developing high-quality RAG systems
RAG has become a standard feature in LLM products. It boosts the quality and relevance of generated content by letting models tap into huge stores of information and integrate external knowledge. The quality of RAG systems can significantly impact the overall product, which makes rigorous evaluation essential. Because the quality of the RAG pipeline is dependent on the retrieved documents that populate the underlying vector database, a thorough evaluation of an LLM system incorporating RAG pipelines should ideally be customized to suit that specific domain and dataset. Quotient’s customized evaluation solution fills this crucial need by enabling developers to measure the effectiveness of their RAG-enabled LLM products accurately.
Quotient and Qdrant enable rapid and precise AI product development using RAG
By integrating Quotient's AI evaluation platform with Qdrant's efficient vector database system, developers gain access to a robust toolset for developing and refining RAG-powered AI products. Here's how our partnership benefits developers:
- Customized and scalable RAG evaluations. Quotient's platform enables developers to measure the impact of their Qdrant-powered RAG applications with realistic, domain-specific tests swiftly and iteratively. This allows for a comprehensive understanding of how RAG influences the overall quality of AI systems and facilitates targeted and relevant AI assessments.
- Faster development of high-quality RAG applications. Qdrant's Hybrid Cloud, built on Kubernetes-native architecture, simplifies the deployment process, giving developers the freedom to select their preferred environment with ease. Combined with Quotient's rapid evaluations, developers can accelerate the development, testing, and deployment of their RAG applications.
- Custom evaluation datasets. Quotient provides developers with a feature unique in the market today, which allows developers to leverage their Qdrant-stored data and automatically create reference datasets. This ultimately enables a streamlined, domain-specific evaluation process.
Let's get started with Qdrant and Quotient 🚀
We're incredibly excited about the possibilities this partnership will bring, and you can start exploring how you’ll create groundbreaking AI solutions with Quotient and Qdrant today.
As part of our partnership, Qdrant users can take advantage of Quotient's features through an expanded free developer tier, with 10x the number of weekly evaluation jobs! We invite them to request access at www.quotientai.co. Remember to add "Qdrant" in the Company section!
Join us on May 7 to learn how you can build high-quality RAG applications with Qdrant and Quotient!
On Tuesday, May 7, join Atita Arora (Solutions Architect, Qdrant) and Deanna Emery (AI Researcher, Quotient) for a webinar, where you’ll learn how Qdrant-enabled RAG applications can be tested and iteratively improved using Quotient.
