LLMs in Your Pipelines

You can easily integrate LLMs in your deepset Cloud pipelines using the versatile PromptNode. PromptNode works well for retrieval augmented generation (RAG) question answering and other NLP tasks such as text classification, summarization, and more. It performs the specific task you define within the prompt you pass to it.

Creating an LLM App

You can start with an out-of-the-box template and then experiment with models and prompts:

1. Create a Pipeline from the template and choose the Retrieval Augmented Generation Question Answering Llama2-13b. It uses PromptNode with the free Llama model by Meta.
   By changing the prompt and the model, you can adjust it to NLP tasks beyond generative question answering.
2. Experiment with your pipeline settings:
   1. Try changing the model. PromptNode works with various models, including GPT-4, Cohere's, or Anthropic's models. For a full list, see PromptNode.
   2. Experiment with different prompts.
      1. PromptNode comes with a set of ready-made prompts for you to try out. Simply pass the prompt name in the default_prompt_template parameter. For a full list of prompts, see Prompt Templates.
      2. Engineer your prompts in Prompt Studio, a sandbox environment in deepset Cloud, to refine and test your prompts.

Learn More

Here's a collection of information you may want to explore for more information related to LLMs and generative AI in deepset Cloud.

About LLMs

• Language Models
• Recommended Models
• RAG Question Answering

Generative AI in Practice

• Use Case: Generative AI Systems
• Tutorial: Building a Summarization System with a Large Language Model
• Tutorial: Building a Robust RAG Question Answering System
• PromptNode
• Using Hosted LLMs in Your Pipelines

Prompt Engineering

• Prompt Engineering Guidelines
• Engineering Prompts