Use Case: Generative AI Systems

Find out when it's best to use a retrieval augmented generation (RAG) system, what type of data it needs, and who it's best for.


Generative AI search systems, such as RAG, can create new, original text based on the documents you feed to them. Unlike extractive question answering systems that highlight the exact answer in the text, generative systems generate coherent and meaningful answers from scratch.

Generative models can perform a variety of NLP tasks, depending on what you tell them to do in the prompt. Some examples include summarization, translation, or question answering. See the sections below for concrete use cases.

Additional Considerations

Before you decide to use a generative model, there are a couple of things you should take into account:

  • Choose a model trained on diverse and unbiased data. This helps to avoid inaccurate or biased responses.
  • Make sure you design and monitor your generative AI system to prevent it from generating inappropriate or harmful content. Use models from reliable vendors or fine-tune your own model. Formulate the prompt text to minimize the risk of prompt injection, for example, by instructing the model to answer with "I don't know" when the answer is not in the documents.
  • Be transparent and make it clear how your system arrives at the answers. In deepset Cloud, you can show the sources for each answer. This helps build user trust in the system.

RAG Systems

A RAG system is best for:

  • Tasks that require the generation of natural language responses to questions.
  • Chatbots, virtual assistants, and knowledge management systems where human-like interaction is important.


You can use any text data. Some examples are:

  • Company policies, benefits, training opportunities, and other HR-related material.
  • Technical documentation and product FAQ


  • Data scientists: Design the QA system, create the pipelines, and supervise domain experts.
  • End users: Use the system, evaluate its usefulness for business, and provide feedback in the deepset Cloud UI.


For examples of RAG pipelines, see Generative Question Answering Pipelines.

What To Do Next?

Create a plipeline, let users test it, and productionize it.