Update readme with links to rag/tool use prompting guide, copying over same changes from unquantized model (#2)

- Update readme with links to rag/tool use prompting guide, copying over same changes from unquantized model (39e50201e2e2d7d5c866648efedb25447bf850bd)


Co-authored-by: Patrick Lewis <patrick-s-h-lewis@users.noreply.huggingface.co>

README.md

@@ -77,9 +77,12 @@ Command-R has been specifically trained with conversational tool use capabilitie
 
 Command-R’s tool use functionality takes a conversation as input (with an optional user-system preamble), along with a list of available tools. The model will then generate a json-formatted list of actions to execute on a subset of those tools. Command-R may use one of its supplied tools more than once. 
 
-The model has been trained to recognise a special `directly_answer` tool, which it uses to indicate that it doesn’t want to use any of its other tools. We recommend including the `directly_answer` tool, but encourage experimentation. 
+The model has been trained to recognise a special `directly_answer` tool, which it uses to indicate that it doesn’t want to use any of its other tools. The ability to abstain from calling a specific tool can be useful in a range of situations, such as greeting a user, or asking clarifying questions.
+We recommend including the `directly_answer` tool, but it can be removed or renamed if required.
 
-Comprehensive documentation and guides on prompting strategies for tool use will be provided shortly.
+Comprehensive documentation for working with command-R's tool use prompt template can be found [here](https://docs.cohere.com/docs/prompting-command-r).
+
+The code snippet below shows a minimal working example on how to render a prompt.
 
 <details>
 <summary><b>Usage: Rendering Tool Use Prompts [CLICK TO EXPAND]</b> </summary>
@@ -201,14 +204,14 @@ Deviating from this prompt template may reduce performance, but we encourage exp
 Command-R’s grounded generation behavior takes a conversation as input (with an optional user-supplied system preamble), along with a list of retrieved document snippets.
 The document snippets should be chunks, rather than long documents, typically around  100-400 words per chunk. Document snippets consist of key-value pairs. The keys should be short descriptive strings, the values can be text or semi-structured.
 
-By default, Command-R will generate grounded responses by first predicting which documents are relevant, then predicting which ones it will cite, then generating an answer. 
+Command-R’s grounded generation behavior takes a conversation as input (with an optional user-supplied system preamble, indicating task, context and desired output style), along with a list of retrieved document snippets.
 Finally, it will then insert grounding spans into the answer. See below for an example. This is referred to as `accurate` grounded generation.
 
 The model is trained with a number of other answering modes, which can be selected by prompt changes . A `fast` citation mode is supported in the tokenizer, which will directly generate an answer with grounding spans in it, without first writing the answer out in full. This sacrifices some grounding accuracy in favor of generating fewer tokens.
 
-The code snippet below shows a minimal working example on how to render a prompt, generate and parse a completion.
+Comprehensive documentation for working with command-R's grounded generation prompt template can be found [here](https://docs.cohere.com/docs/prompting-command-r).
 
-Comprehensive documentation and guides on prompting strategies on grounded generation will be provided in follow-ups at a later stage.
+The code snippet below shows a minimal working example on how to render a prompt.
 
 <details>
 <summary> <b>Usage: Rendering Grounded Generation prompts [CLICK TO EXPAND]</b> </summary>