Pryon Generative Engine offers developers a flexible, reliable, and LLM-agnostic toolkit for implementing generative orchestration based on trusted content.
Smooth, intuitive responses to users’ queries that point to source documents
Accommodates any open or closed Large Language Model (LLM)
Mitigates the security and hallucination risks of public generative tools
Pryon Generative Engine gives developers the flexibility to choose a public, custom, or Pryon-developed LLM.
Pryon Generative Engine is further customizable, enabling developers to define when and how to use the retrieval and generative elements of the Pryon RAG Suite. For example, developers can prompt the LLMs with custom context and information to ensure high quality outputs.
Pryon Generative Engine helps developers choose the LLM that best meets their needs. You can compare the performance of models across various measures, such as speed and accuracy.
The best way to ensure users receive accurate, helpful, and relevant responses is to collect feedback. Pryon Generative Engine enables developers to let users rate a response with a simple and intuitive “thumbs up/thumbs down” reaction.
Content experts and subject matter experts (SMEs) can review these reactions and take appropriate action — for instance, by feeding new and updated content to the Ingestion Engine.
Pryon Retrieval Engine has out-of-domain detection, so it knows when a query is asking for irrelevant information. Pryon Generative Engine then intelligently tells the user that it can’t answer the question, instead of making up (hallucinating) a response.
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Yes. Pryon Generative Engine is compatible with public, custom, and Pryon-developed LLMs. These include GPT-4, LLaMA 2, LLaMA 3, and Mistral.
Yes. Each component of Pryon RAG Suite is designed to work seamlessly together to provide a best-in-class RAG solution for developers.
Pryon Generative Engine offers a safe and trustworthy pathway to implement enterprise-grade generative AI. Since the Generative Engine prompts the LLMs to only produce output based on an organization’s trusted content, Pryon’s approach to RAG mitigates the security and hallucination risks of public generative tools. At the end of the generative process, users receive smooth, succinct, and trustworthy answers that point to source documents for verification.