I have used Cohere Command R mainly for Retrieval-Augmented Generation (RAG) workflows where the model needs to answer questions from enterprise documents rather than relying on its pre-trained knowledge. The primary use cases are document Q&A, chatbot-style assistant, summarizing longer internal content, and providing grounded responses with citations. It is particularly useful when I wanted the model to retrieve the right context from PDFs, similar to what Gemini does in Google Drive. Additionally, it works well with knowledge-based articles, technical documentations, or policy documents to generate concise answers along with source citations, making it clear to the end user where the answers are coming from. When I have a document and ask something related to that document, my answer is mostly from the document only. When there is a source citation that indicates it fetched something from that content or paragraphs, it becomes easy to navigate if anything goes wrong. This feature of source citation is among the best. For the context part, when I am asking a longer and repetitive question, it maintains the context of what I was asking, which I find beneficial. I would recommend Cohere Command R if your main use case is RAG, document Q&A, and enterprise knowledge assistance, or if you are building any chatbot workflows where citations and grounded answers matter. It strongly fits when you already have a plan to build proper retrieval pipelines.
On my platform, there were many users who wanted answers from their documents. They had many large-sized documents like PDFs of 25 pages, and some users had PDFs of 150 pages. Using a normal RAG pipeline was very complex for them to get better answers. When we deployed Cohere Command R on our platform, many of our users uploaded their documents and used this model, which gave much better accuracy compared to other models. This was a very good achievement for integrating this model into our platform. The strong tool call use of this model is also very good. With Retrieval-Augmented Generation, this model performs better tool calling as well. I can give you some complex examples that users were asking about. Some of my users were NEET students getting their doctor degrees, so their documents had answers related to the human body. There were multiple paragraphs in which the same thing was repeated. They uploaded their book and wanted to learn from it using this model. This model was able to give correct answers from the particular sections. There were mismatches in other models because the same content was referred to somewhere else in another part of the book, but it was not intended to give the answer. This model excelled here. Some of my users had large documents and needed fast answers, with less accuracy being acceptable as long as they got fast responses. They uploaded their documents and received fast answers because they trained their chatbots on the platform. They uploaded FAQ questions and this model was able to give very fast answers according to their document FAQ questions. Many of our users came with PDFs and docs needing to build a chatbot for their FAQs on their website. They wanted a chatbot where users could come, create an agent, and load their script on that platform. To train the agent, many of their use cases required providing a chatbot on their website where users could ask FAQ questions. Using Cohere Command R, they were able to upload FAQ questions as a document and this model read the FAQ questions and returned the answer. Many of our chatbots are using Cohere Command R and it returns answers according to user FAQ questions. The accuracy is good. The context window is small, which is good for the FAQ part only because we do not need large context output for FAQ chatbots. The main focus of my company related to Cohere Command R is that we are pitching this model for chatbot selling only. After this model release, when we integrated this model on our platform, around 20% of users came to use chatbot. Around 20% of the users were using this FAQ chatbot using Cohere Command R. Previously they were facing complaints that the chatbot replied too slowly or the chatbot hallucinated a lot, meaning it gave random answers. The users were complaining, but after using this model, the complaints are very minimal and their support tickets are reduced by 5% to 10%.
I implemented it myself in my bot, defining what is acceptable based on how people chat and what they might mean when they say certain things. I never actually used that feature for Cohere Command R. The dataset I am using is just the chat, the user chat, and it is not that big. It is just a few months, and I always clear the chats after a few months. So it is just normal content, nothing extraordinary; I do not think it can be quantified as big data.
Cohere Command R offers a sophisticated suite of AI-driven tools tailored for advanced data analysis and language comprehension, making it a go-to option for businesses seeking efficiency.
Cohere Command R stands out as a versatile AI tool equipped to handle complex linguistic tasks and streamline data operations. It's designed to support developers and businesses in deriving actionable insights through advanced AI capabilities. With its robust performance, it caters to the growing needs of...
I have used Cohere Command R mainly for Retrieval-Augmented Generation (RAG) workflows where the model needs to answer questions from enterprise documents rather than relying on its pre-trained knowledge. The primary use cases are document Q&A, chatbot-style assistant, summarizing longer internal content, and providing grounded responses with citations. It is particularly useful when I wanted the model to retrieve the right context from PDFs, similar to what Gemini does in Google Drive. Additionally, it works well with knowledge-based articles, technical documentations, or policy documents to generate concise answers along with source citations, making it clear to the end user where the answers are coming from. When I have a document and ask something related to that document, my answer is mostly from the document only. When there is a source citation that indicates it fetched something from that content or paragraphs, it becomes easy to navigate if anything goes wrong. This feature of source citation is among the best. For the context part, when I am asking a longer and repetitive question, it maintains the context of what I was asking, which I find beneficial. I would recommend Cohere Command R if your main use case is RAG, document Q&A, and enterprise knowledge assistance, or if you are building any chatbot workflows where citations and grounded answers matter. It strongly fits when you already have a plan to build proper retrieval pipelines.
On my platform, there were many users who wanted answers from their documents. They had many large-sized documents like PDFs of 25 pages, and some users had PDFs of 150 pages. Using a normal RAG pipeline was very complex for them to get better answers. When we deployed Cohere Command R on our platform, many of our users uploaded their documents and used this model, which gave much better accuracy compared to other models. This was a very good achievement for integrating this model into our platform. The strong tool call use of this model is also very good. With Retrieval-Augmented Generation, this model performs better tool calling as well. I can give you some complex examples that users were asking about. Some of my users were NEET students getting their doctor degrees, so their documents had answers related to the human body. There were multiple paragraphs in which the same thing was repeated. They uploaded their book and wanted to learn from it using this model. This model was able to give correct answers from the particular sections. There were mismatches in other models because the same content was referred to somewhere else in another part of the book, but it was not intended to give the answer. This model excelled here. Some of my users had large documents and needed fast answers, with less accuracy being acceptable as long as they got fast responses. They uploaded their documents and received fast answers because they trained their chatbots on the platform. They uploaded FAQ questions and this model was able to give very fast answers according to their document FAQ questions. Many of our users came with PDFs and docs needing to build a chatbot for their FAQs on their website. They wanted a chatbot where users could come, create an agent, and load their script on that platform. To train the agent, many of their use cases required providing a chatbot on their website where users could ask FAQ questions. Using Cohere Command R, they were able to upload FAQ questions as a document and this model read the FAQ questions and returned the answer. Many of our chatbots are using Cohere Command R and it returns answers according to user FAQ questions. The accuracy is good. The context window is small, which is good for the FAQ part only because we do not need large context output for FAQ chatbots. The main focus of my company related to Cohere Command R is that we are pitching this model for chatbot selling only. After this model release, when we integrated this model on our platform, around 20% of users came to use chatbot. Around 20% of the users were using this FAQ chatbot using Cohere Command R. Previously they were facing complaints that the chatbot replied too slowly or the chatbot hallucinated a lot, meaning it gave random answers. The users were complaining, but after using this model, the complaints are very minimal and their support tickets are reduced by 5% to 10%.
I implemented it myself in my bot, defining what is acceptable based on how people chat and what they might mean when they say certain things. I never actually used that feature for Cohere Command R. The dataset I am using is just the chat, the user chat, and it is not that big. It is just a few months, and I always clear the chats after a few months. So it is just normal content, nothing extraordinary; I do not think it can be quantified as big data.
My main use case for Cohere Command R is for a GenAI application. For the RAG project, we are using Cohere Command R for the retrieval process.