Customer support · Production
Cohere Command Model Use Case Patterns: Writing, QA, Brainstorming, Summarizing, Extracting
The problem
Developers starting with LLM technology find it daunting to comprehend what is possible because general-purpose models can be applied in infinite ways, and without recognising the patterns it can feel overwhelming.
Workflow diagram · grounded in source
1
Install and configure Cohere client
integration
“let's install the Cohere package, get the Cohere API key, and set up the client”
2
Construct contextual prompt
trigger
“create a variable for the user to input some text and merge that, together with the product description, into the main prompt”
3
Command model generates text
ai_action
“call the Chat endpoint, which is how we can access the Command model”
4
Writing: email from bullet points
output
“Create an email about the product above mentioning the following”
5
Extractive QA from knowledge base
ai_action
“we can get the model to refer to specific knowledge bases to help it do its job well”
6
Information extraction from email
ai_action
“Extract the product, refund reason and pick-up address from this email”
7
Summarization of customer reviews
ai_action
“Summarize the following”
Reported outcome
(not stated)
Reported stack
CommandCohereGoogle Colaboratory
Frequently asked questions
What did this team achieve with this AI workflow?
(not stated)
What tools did this team use?
Command, Cohere, Google Colaboratory.
How is this customer support AI workflow structured?
Install and configure Cohere client → Construct contextual prompt → Command model generates text → Writing: email from bullet points → Extractive QA from knowledge base → Information extraction from email → Summarization of customer reviews.