Chat with Retrieval-Augmented Generation (RAG) offers a powerful way to integrate conversational AI into your apps. By leveraging retrieval-augmented generation, it combines inputs, sources, and models to create more impactful product experiences.
One of the key features is its ability to retrieve information from various sources. It can point the model at enterprise datastores, allowing it to cite proprietary data seamlessly and securely. When directed at the internet, it can generate responses based on real-time information. And with specific documents, it enables grounded Q&A on particular content.
Conversation is at the heart of this tool. It understands the intent behind messages, remembers conversation history, and responds intelligently through multi-turn conversations. The responses are powered by Cohere's Command model.
Reducing hallucinations is another important aspect. With grounding and citations, it builds trust between the generated responses and users, as they can see where the responses are coming from. Command is also trained to answer questions from additional sources.
Privacy is well taken care of. When privately deployed, the training data, input prompts, and output responses remain private within your secure environment.
Regardless of your experience level with ML/AI, Cohere’s Command model makes it easy to build chat interfaces in your applications. You can get started with simple APIs and achieve powerful results.
In conclusion, Chat with RAG provides a comprehensive solution for enhancing chat capabilities in apps, offering features like information retrieval, intelligent conversation handling, hallucination reduction, and privacy protection.
