GenAI has a trust problem—hallucinations, governance risks, and unreliable outputs. Retrieval-Augmented Generation (RAG) provides domain-specific data to enhance accuracy, governance, and performance. Learn how vector, relational, and graph RAG improve AI outcomes.
speculate or fabricate responses due to probabilistic reasoning
with hallucinations, governance risks, and inaccurate results
remains untapped and difficult to integrate into AI workflows
RAG or Retrieval-Augmented Generation is like an “Open-Book Test” for AI.
✅ RAG provides a strong foundation for Responsible AI.
Vespa delivers all the building blocks of an AI application, including vector database, hybrid search, retrieval augmented generation (RAG), natural language processing (NLP), machine learning, and support for large language models (LLM).
Build AI applications that meet your requirements precisely. Seamlessly integrate your operational systems and databases using Vespa’s APIs and SDKs, ensuring efficient integration without redundant data duplication.
Achieve precise, relevant results using Vespa’s hybrid search capabilities, which combine multiple data types—vectors, text, structured, and unstructured data. Machine learning algorithms rank and score results to ensure they meet user intent and maximize relevance.
Enhance content analysis with NLP through advanced text retrieval, vector search with embeddings and integration with custom or pre-trained machine learning models. Vespa enables efficient semantic search, allowing users to match queries to documents based on meaning rather than just keywords.
Search and retrieve data using detailed contextual clues that combine images and text. By enhancing the cross-referencing of posts, images, and descriptions, Vespa makes retrieval more intelligent and visually intuitive, transforming search into a seamless, human-like experience.
Ensure seamless user experience and reduce management costs with Vespa Cloud. Applications dynamically adjust to fluctuating loads, optimizing performance and cost to eliminate the need for over-provisioning.
Deliver instant results through Vespa’s distributed architecture, efficient query processing, and advanced data management. With optimized low-latency query execution, real-time data updates, and sophisticated ranking algorithms, Vespa actions data with AI across the enterprise.
Deliver services without interruption with Vespa’s high availability and fault-tolerant architecture, which distributes data, queries, and machine learning models across multiple nodes.
Bring computation to the data distributed across multiple nodes. Vespa reduces network bandwidth costs, minimizes latency from data transfers, and ensures your AI applications comply with existing data residency and security policies. All internal communications between nodes are secured with mutual authentication and encryption, and data is further protected through encryption at rest.
Reduces hallucinations by grounding AI in real data.
Automates AI-assisted workflows with governed, reliable outputs.
Delivers more useful AI responses, reducing manual corrections.
Enables AI-driven sales, customer service, and decision-making.
Ensures governance, ethics, and compliance.
Better AI adoption, improved automation, and increased ROI.
Start with Low-Risk Projects – Pilot RAG internally before scaling to external use cases.
Choose the Right RAG Approach – Vector, Relational, or Graph (or a hybrid).
Combine vector search + keyword search for stronger results.
Platforms like Vespa.ai streamline hybrid RAG implementations.
Track performance, adjust retrieval strategies, and fine-tune outputs.
Vespa.ai –Vespa.ai is a platform for developing and running large-scale enterprise AI applications, using big data, RAG, vector search, machine learning and LLMs to deliver fast, precise decisions that drive business success.
BARC (Business Application Research Center) – Europe’s top analyst firm for AI, analytics, and business intelligence research.
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