Generative AI

LLMs have arrived on the NLP stage with a bang, and it's clear that they're here to stay. But to properly integrate their generative AI capabilities into a production environment, it's not enough to simply connect to an LLM API and hope for the best. When used properly, these models can create valuable and secure user interactions based on your organization's own data.

With retrieval augmented generation (RAG), you can ensure that the LLM bases its responses on the data you want. This greatly reduces the risk of hallucinations, and allows you to build a customized user experience: instead of relying on its vast and mixed quality training data, the model can leverage your organization's unique knowledge for the benefit of the users.

Prompting, on the other hand, gives you control over the shape of the model's output: do you want the LLM to return short answers or elaborate ones? Should its tone be polite, humorous, or emphatically matter-of-fact? Do you want it to illustrate its answers with examples? With LLMs, the possibilities for customization are endless.

deepset Cloud, our model-agnostic LLM platform for AI teams, gives you all the tools you need to build a working RAG system in no time. Try different models for both document retrieval and text generation, connect them to your own data, and show your stakeholders different prototypes to experiment with right in the browser.

What’s more, you have access to a library of curated prompts that you can try and compare live in a sandbox environment to see which one delivers the best results for your use case.

The platform’s ease of use belies a flexible and powerful backend that allows you to deploy your RAG system to production. Once deployed, your application will benefit from deepset Cloud’s automated scaling and its query retries to ensure your users have a safe and valuable experience with generative AI, powered by your own data.

If you want to have more detailed information on the above, please contact us here.