Prompts embedded in code slow down your ability to test and learn better prompts. And good prompts should be easy to share. Enable prompt engineers and developers to work together in getting the best outcome for your LLM application.
Prompts have inputs, which means 'garbage in, garbage out'. To ensure that prompts receive the inputs they expect, prompts are configured to validate inputs. Similar validations may be applied to outputs such as ensuring valid JSON that conforms to the expected schema.
Feature Store Integration
In addition to the user input, prompts can be augmented with data features from an online feature store or search results from a vector database. This may be used to personalize the generated content for one-to-one marketing or customer service, or to enable responses to draw from current or organization-specific facts.
Get started in minutes
Everything you need to start using LLMs in your applications.
Scale to support teams
Enable collaboration between application developers, prompt engineers, API designers, data engineers, and data scientists.
Manage integrations with LLM models to validate inputs and outputs, add guardrails, and support auditability and cost tracking..
Stay in the loop
Monitor usage and collect feedback signals to evaluate the effectiveness of prompt versions. Enable continuous improvement.
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The application is open source. It is being used to support a live application but is still in an early stage. The API may change as the architecture and features evolve. Join the conversation.