RAG on watsonx.ai

Build your own RAG pipeline for better model accuracy and performance

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Streamline RAG application building

Use foundation models to build, optimize and deploy retrieval augmented generation (RAG) pipelines using your enterprise knowledge base.

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Cost optimization

Infer on a smaller, specialized model, not a larger generic model.
Enterprise-grade

Built with security, scalability and compliance in mind.
Accuracy and performance

Ground your applications in a knowledge base to improve application outputs.
Rapid deployment

Go from concept to production in days, not months.
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IBM is named a Leader in Data Science & Machine Learning

IBM has been recognized as a Leader in the 2025 Gartner® Magic Quadrant™ for Data Science and Machine Learning Platforms.

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Chat with documents

Chat with documents enables AI builders to quickly create document-grounded RAG solutions for fast prototyping or deployment. By using the no-code Prompt Lab in IBM® watsonx.ai®, users can upload and configure PDFs, Word docs, and more with ease. Developers can scale with vector stores such as Milvus or Elasticsearch to improve grounding accuracy. Deploy as an application programming interface (API) for AI assistants or agents.
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AutoAI RAG

AutoAI for RAG simplifies pipeline building by automatically generating various pipeline configurations. It then evaluates and ranks their performance, presenting the best options on a leaderboard. A process that might traditionally take months—exhausting hundreds of potential combinations—is now streamlined for completion.
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Learn more: IBM Named ML Ops Leader by IDC Forrester Names IBM a Strong Performer in AI/ML Platforms