Prompt & RAG Labs
Turning AI experiments into business outcomes faster by continuously iterating, evaluating and optimizing retrieval and generation in one environment, while reducing trial and error and accelerating production-ready AI.
Iterate Faster with AI That Drives Real Business Value
Prompt and RAG Labs provide developers with a modular, low/no-code environment to rapidly test, evaluate and refine retrieval and generation pipelines for different AI experiences. Instead of rebuilding systems for every use case, teams can iterate on prompts, models and retrieval strategies in one platform, accelerating time to value while delivering AI that performs in real-world business scenarios.
A Modular Approach to Tuning AI Experiences
Unlike rigid AI tools, Prompt and RAG Labs allow every part of the retrieval-augmented generation (RAG) pipeline—from embeddings to models—to be tuned independently and reused across AI experiences.
Rapid, Parallel Pipeline Iteration
Prompt and RAG Labs work in tandem so you can:
- Test and compare multiple RAG pipelines at once without rebuilding infrastructure, dramatically shortening iteration cycles
- Deliver production-ready AI experiences faster by identifying what works before writing custom code
No/Low-Code Optimization Without Loss of Control
Prompt and RAG Labs enable users to:
- Iterate quickly using no/low-code tools while retaining full control over the underlying RAG and LLM configurations
- Minimize engineering overhead and free up developers to focus on building differentiated AI experiences, not plumbing
Key Benefits
Faster Validation Before Production
Prompt and RAG Labs let developers validate retrieval and generation behavior early using real data and questions. By testing pipelines before deployment, teams reduce costly rework, avoid regressions and ship AI experiences with greater confidence and predictability.
Reusable Configurations Across Use Cases
Developers can save, reuse and adapt high-performing configurations across multiple AI experiences. This eliminates redundant experimentation, accelerates project delivery and fosters consistency in how AI behaves across applications, teams and environments.
Reduced Engineering Overhead
Built-in tooling replaces custom scripts, manual evaluations and on-off experiments so developers spend less time maintaining RAG infrastructure and more time building differentiated AI experiences that deliver real business value.
Frequently Asked Questions
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