diff --git a/docs/source/en/chat_templating.md b/docs/source/en/chat_templating.md
index 115c9f51677..82bdf591ae5 100644
--- a/docs/source/en/chat_templating.md
+++ b/docs/source/en/chat_templating.md
@@ -20,25 +20,11 @@ rendered properly in your Markdown viewer.
 
 An increasingly common use case for LLMs is **chat**. In a chat context, rather than continuing a single string
 of text (as is the case with a standard language model), the model instead continues a conversation that consists
-of one or more **messages**, each of which includes a **role** as well as message text.
+of one or more **messages**, each of which includes a **role**, like "user" or "assistant", as well as message text.
 
-Most commonly, these roles are "user" for messages sent by the user, and "assistant" for messages sent by the model.
-Some models also support a "system" role. System messages are usually sent at the beginning of the conversation
-and include directives about how the model should behave in the subsequent chat.
-
-All language models, including models fine-tuned for chat, operate on linear sequences of tokens and do not intrinsically
-have special handling for roles. This means that role information is usually injected by adding control tokens
-between messages, to indicate both the message boundary and the relevant roles.
-
-Unfortunately, there isn't (yet!) a standard for which tokens to use, and so different models have been trained
-with wildly different formatting and control tokens for chat. This can be a real problem for users - if you use the
-wrong format, then the model will be confused by your input, and your performance will be a lot worse than it should be.
-This is the problem that **chat templates** aim to resolve. 
-
-Chat conversations are typically represented as a list of dictionaries, where each dictionary contains `role`
-and `content` keys, and represents a single chat message. Chat templates are strings containing a Jinja template that
-specifies how to format a conversation for a given model into a single tokenizable sequence. By storing this information
-with the tokenizer, we can ensure that models get input data in the format they expect.
+Much like tokenization, different models expect very different input formats for chat. This is the reason we added
+**chat templates** as a feature. Chat templates are part of the tokenizer. They specify how to convert conversations, 
+represented as lists of messages, into a single tokenizable string in the format that the model expects. 
 
 Let's make this concrete with a quick example using the `BlenderBot` model. BlenderBot has an extremely simple default 
 template, which mostly just adds whitespace between rounds of dialogue:
@@ -48,9 +34,9 @@ template, which mostly just adds whitespace between rounds of dialogue:
 >>> tokenizer = AutoTokenizer.from_pretrained("facebook/blenderbot-400M-distill")
 
 >>> chat = [
-...   {"role": "user", "content": "Hello, how are you?"},
-...   {"role": "assistant", "content": "I'm doing great. How can I help you today?"},
-...   {"role": "user", "content": "I'd like to show off how chat templating works!"},
+...    {"role": "user", "content": "Hello, how are you?"},
+...    {"role": "assistant", "content": "I'm doing great. How can I help you today?"},
+...    {"role": "user", "content": "I'd like to show off how chat templating works!"},
 ... ]
 
 >>> tokenizer.apply_chat_template(chat, tokenize=False)
@@ -59,28 +45,196 @@ template, which mostly just adds whitespace between rounds of dialogue:
 
 Notice how the entire chat is condensed into a single string. If we use `tokenize=True`, which is the default setting,
 that string will also be tokenized for us. To see a more complex template in action, though, let's use the 
-`meta-llama/Llama-2-7b-chat-hf` model. Note that this model has gated access, so you will have to
-[request access on the repo](https://huggingface.co/meta-llama/Llama-2-7b-chat-hf) if you want to run this code yourself:
+`mistralai/Mistral-7B-Instruct-v0.1` model.
 
 ```python
->> from transformers import AutoTokenizer
->> tokenizer = AutoTokenizer.from_pretrained("meta-llama/Llama-2-7b-chat-hf")
+>>> from transformers import AutoTokenizer
+>>> tokenizer = AutoTokenizer.from_pretrained("mistralai/Mistral-7B-Instruct-v0.1")
 
->> chat = [
+>>> chat = [
 ...   {"role": "user", "content": "Hello, how are you?"},
 ...   {"role": "assistant", "content": "I'm doing great. How can I help you today?"},
 ...   {"role": "user", "content": "I'd like to show off how chat templating works!"},
 ... ]
 
->> tokenizer.use_default_system_prompt = False
->> tokenizer.apply_chat_template(chat, tokenize=False)
-"<s>[INST] Hello, how are you? [/INST] I'm doing great. How can I help you today? </s><s>[INST] I'd like to show off how chat templating works! [/INST]"
+>>> tokenizer.apply_chat_template(chat, tokenize=False)
+"<s>[INST] Hello, how are you? [/INST]I'm doing great. How can I help you today?</s> [INST] I'd like to show off how chat templating works! [/INST]"
 ```
 
 Note that this time, the tokenizer has added the control tokens [INST] and [/INST] to indicate the start and end of 
-user messages (but not assistant messages!)
+user messages (but not assistant messages!). Mistral-instruct was trained with these tokens, but BlenderBot was not.
 
-## How do chat templates work?
+## How do I use chat templates?
+
+As you can see in the example above, chat templates are easy to use. Simply build a list of messages, with `role`
+and `content` keys, and then pass it to the [`~PreTrainedTokenizer.apply_chat_template`] method. Once you do that,
+you'll get output that's ready to go! When using chat templates as input for model generation, it's also a good idea
+to use `add_generation_prompt=True` to add a [generation prompt](#what-are-generation-prompts). 
+
+Here's an example of preparing input for `model.generate()`, using the `Zephyr` assistant model:
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+checkpoint = "HuggingFaceH4/zephyr-7b-beta"
+tokenizer = AutoTokenizer.from_pretrained(checkpoint)
+model = AutoModelForCausalLM.from_pretrained(checkpoint)  # You may want to use bfloat16 and/or move to GPU here
+
+messages = [
+    {
+        "role": "system",
+        "content": "You are a friendly chatbot who always responds in the style of a pirate",
+    },
+    {"role": "user", "content": "How many helicopters can a human eat in one sitting?"},
+ ]
+tokenized_chat = tokenizer.apply_chat_template(messages, tokenize=True, add_generation_prompt=True, return_tensors="pt")
+print(tokenizer.decode(tokenized_chat[0]))
+```
+This will yield a string in the input format that Zephyr expects. 
+```text
+<|system|>
+You are a friendly chatbot who always responds in the style of a pirate</s> 
+<|user|>
+How many helicopters can a human eat in one sitting?</s> 
+<|assistant|>
+```
+
+Now that our input is formatted correctly for Zephyr, we can use the model to generate a response to the user's question:
+
+```python
+outputs = model.generate(tokenized_chat, max_new_tokens=128) 
+print(tokenizer.decode(outputs[0]))
+```
+
+This will yield:
+
+```text
+<|system|>
+You are a friendly chatbot who always responds in the style of a pirate</s> 
+<|user|>
+How many helicopters can a human eat in one sitting?</s> 
+<|assistant|>
+Matey, I'm afraid I must inform ye that humans cannot eat helicopters. Helicopters are not food, they are flying machines. Food is meant to be eaten, like a hearty plate o' grog, a savory bowl o' stew, or a delicious loaf o' bread. But helicopters, they be for transportin' and movin' around, not for eatin'. So, I'd say none, me hearties. None at all.
+```
+
+Arr, 'twas easy after all!
+
+## Is there an automated pipeline for chat?
+
+Yes, there is: [`ConversationalPipeline`]. This pipeline is designed to make it easy to use chat models. Let's try
+the `Zephyr` example again, but this time using the pipeline:
+
+```python
+from transformers import pipeline
+
+pipe = pipeline("conversational", "HuggingFaceH4/zephyr-7b-beta")
+messages = [
+    {
+        "role": "system",
+        "content": "You are a friendly chatbot who always responds in the style of a pirate",
+    },
+    {"role": "user", "content": "How many helicopters can a human eat in one sitting?"},
+]
+print(pipe(messages))
+```
+
+```text
+Conversation id: 76d886a0-74bd-454e-9804-0467041a63dc
+system: You are a friendly chatbot who always responds in the style of a pirate
+user: How many helicopters can a human eat in one sitting?
+assistant: Matey, I'm afraid I must inform ye that humans cannot eat helicopters. Helicopters are not food, they are flying machines. Food is meant to be eaten, like a hearty plate o' grog, a savory bowl o' stew, or a delicious loaf o' bread. But helicopters, they be for transportin' and movin' around, not for eatin'. So, I'd say none, me hearties. None at all.
+```
+
+[`ConversationalPipeline`] will take care of all the details of tokenization and calling `apply_chat_template` for you -
+once the model has a chat template, all you need to do is initialize the pipeline and pass it the list of messages!
+
+## What are "generation prompts"?
+
+You may have noticed that the `apply_chat_template` method has an `add_generation_prompt` argument. This argument tells
+the template to add tokens that indicate the start of a bot response. For example, consider the following chat:
+
+```python
+messages = [
+    {"role": "user", "content": "Hi there!"},
+    {"role": "assistant", "content": "Nice to meet you!"},
+    {"role": "user", "content": "Can I ask a question?"}
+]
+```
+
+Here's what this will look like without a generation prompt, using the ChatML template we saw in the Zephyr example:
+
+```python
+tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=False)
+"""<|im_start|>user
+Hi there!<|im_end|>
+<|im_start|>assistant
+Nice to meet you!<|im_end|>
+<|im_start|>user
+Can I ask a question?<|im_end|>
+"""
+```
+
+And here's what it looks like **with** a generation prompt:
+
+```python
+tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
+"""<|im_start|>user
+Hi there!<|im_end|>
+<|im_start|>assistant
+Nice to meet you!<|im_end|>
+<|im_start|>user
+Can I ask a question?<|im_end|>
+<|im_start|>assistant
+"""
+```
+
+Note that this time, we've added the tokens that indicate the start of a bot response. This ensures that when the model
+generates text it will write a bot response instead of doing something unexpected, like continuing the user's 
+message. Remember, chat models are still just language models - they're trained to continue text, and chat is just a 
+special kind of text to them! You need to guide them with the appropriate control tokens so they know what they're 
+supposed to be doing.
+
+Not all models require generation prompts. Some models, like BlenderBot and LLaMA, don't have any
+special tokens before bot responses. In these cases, the `add_generation_prompt` argument will have no effect. The exact
+effect that `add_generation_prompt` has will depend on the template being used.
+
+## Can I use chat templates in training?
+
+Yes! We recommend that you apply the chat template as a preprocessing step for your dataset. After this, you
+can simply continue like any other language model training task. When training, you should usually set 
+`add_generation_prompt=False`, because the added tokens to prompt an assistant response will not be helpful during 
+training. Let's see an example:
+
+```python
+from transformers import AutoTokenizer
+from datasets import Dataset
+
+tokenizer = AutoTokenizer.from_pretrained("HuggingFaceH4/zephyr-7b-beta")
+
+chat1 = [
+    {"role": "user", "content": "Which is bigger, the moon or the sun?"},
+    {"role": "assistant", "content": "The sun."}
+]
+chat2 = [
+    {"role": "user", "content": "Which is bigger, a virus or a bacterium?"},
+    {"role": "assistant", "content": "A bacterium."}
+]
+
+dataset = Dataset.from_dict({"chat": [chat1, chat2]})
+dataset = dataset.map(lambda x: {"formatted_chat": tokenizer.apply_chat_template(x["chat"], tokenize=False, add_generation_prompt=False)})
+print(dataset['formatted_chat'][0])
+```
+And we get:
+```text
+<|user|>
+Which is bigger, the moon or the sun?</s>
+<|assistant|>
+The sun.</s>
+```
+
+From here, just continue training like you would with a standard language modelling task, using the `formatted_chat` column.
+
+## Advanced: How do chat templates work?
 
 The chat template for a model is stored on the `tokenizer.chat_template` attribute. If no chat template is set, the
 default template for that model class is used instead. Let's take a look at the template for `BlenderBot`:
@@ -154,7 +308,9 @@ Hopefully if you stare at this for a little bit you can see what this template i
 on the "role" of each message, which represents who sent it. User, assistant and system messages are clearly
 distinguishable to the model because of the tokens they're wrapped in.
 
-## How do I create a chat template?
+## Advanced: Adding and editing chat templates
+
+### How do I create a chat template?
 
 Simple, just write a jinja template and set `tokenizer.chat_template`. You may find it easier to start with an 
 existing template from another model and simply edit it for your needs! For example, we could take the LLaMA template
@@ -187,7 +343,7 @@ tokenizer.push_to_hub("model_name")  # Upload your new template to the Hub!
 The method [`~PreTrainedTokenizer.apply_chat_template`] which uses your chat template is called by the [`ConversationalPipeline`] class, so 
 once you set the correct chat template, your model will automatically become compatible with [`ConversationalPipeline`].
 
-## What are "default" templates?
+### What are "default" templates?
 
 Before the introduction of chat templates, chat handling was hardcoded at the model class level. For backwards 
 compatibility, we have retained this class-specific handling as default templates, also set at the class level. If a
@@ -200,7 +356,7 @@ the class template is appropriate for your model, we strongly recommend overridi
 setting the `chat_template` attribute explicitly to make it clear to users that your model has been correctly configured
 for chat, and to future-proof in case the default templates are ever altered or deprecated.
 
-## What template should I use?
+### What template should I use?
 
 When setting the template for a model that's already been trained for chat, you should ensure that the template
 exactly matches the message formatting that the model saw during training, or else you will probably experience
@@ -229,7 +385,7 @@ tokenizer.chat_template = "{% if not add_generation_prompt is defined %}{% set a
 This template wraps each message in `<|im_start|>` and `<|im_end|>` tokens, and simply writes the role as a string, which
 allows for flexibility in the roles you train with. The output looks like this:
 
-```
+```text
 <|im_start|>system
 You are a helpful chatbot that will do its best not to say anything so stupid that people tweet about it.<|im_end|>
 <|im_start|>user
@@ -242,62 +398,12 @@ The "user", "system" and "assistant" roles are the standard for chat, and we rec
 particularly if you want your model to operate well with [`ConversationalPipeline`]. However, you are not limited
 to these roles - templating is extremely flexible, and any string can be a role.
 
-## What are "generation prompts"?
-
-You may notice that the `apply_chat_template` method has an `add_generation_prompt` argument. This argument tells
-the template to add tokens that indicate the start of a bot response. For example, consider the following chat:
-
-```python
-messages = [
-    {"role": "user", "content": "Hi there!"},
-    {"role": "assistant", "content": "Nice to meet you!"},
-    {"role": "user", "content": "Can I ask a question?"}
-]
-```
-
-Here's what this will look like without a generation prompt, using the ChatML template we described above:
-
-```python
->> tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=False)
-"""<|im_start|>user
-Hi there!<|im_end|>
-<|im_start|>assistant
-Nice to meet you!<|im_end|>
-<|im_start|>user
-Can I ask a question?<|im_end|>
-"""
-```
-
-And here's what it looks like **with** a generation prompt:
-
-```python
->> tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
-"""<|im_start|>user
-Hi there!<|im_end|>
-<|im_start|>assistant
-Nice to meet you!<|im_end|>
-<|im_start|>user
-Can I ask a question?<|im_end|>
-<|im_start|>assistant
-"""
-```
-
-Note that this time, we've added the tokens that indicate the start of a bot response. This ensures that when the model
-generates text it will write a bot response instead of doing something unexpected, like continuing the user's 
-message. Remember, chat models are still just language models - they're trained to continue text, and chat is just a 
-special kind of text to them! You need to guide them with the appropriate control tokens so they know what they're 
-supposed to be doing.
-
-Not all models require generation prompts. Some models, like BlenderBot and LLaMA, don't have any
-special tokens before bot responses. In these cases, the `add_generation_prompt` argument will have no effect. The exact
-effect that `add_generation_prompt` has will depend on the template being used.
-
-## I want to use chat templates! How should I get started?
+### I want to add some chat templates! How should I get started?
 
 If you have any chat models, you should set their `tokenizer.chat_template` attribute and test it using
-[`~PreTrainedTokenizer.apply_chat_template`]. This applies even if you're not the model owner - if you're using a model
-with an empty chat template, or one that's still using the default class template, please open a [pull request](https://huggingface.co/docs/hub/repositories-pull-requests-discussions) to
-the model repository so that this attribute can be set properly!
+[`~PreTrainedTokenizer.apply_chat_template`], then push the updated tokenizer to the Hub. This applies even if you're
+not the model owner - if you're using a model with an empty chat template, or one that's still using the default class
+template, please open a [pull request](https://huggingface.co/docs/hub/repositories-pull-requests-discussions) to the model repository so that this attribute can be set properly!
 
 Once the attribute is set, that's it, you're done! `tokenizer.apply_chat_template` will now work correctly for that
 model, which means it is also automatically supported in places like `ConversationalPipeline`!
@@ -306,7 +412,7 @@ By ensuring that models have this attribute, we can make sure that the whole com
 open-source models. Formatting mismatches have been haunting the field and silently harming performance for too long - 
 it's time to put an end to them!
 
-## Template writing tips
+## Advanced: Template writing tips
 
 If you're unfamiliar with Jinja, we generally find that the easiest way to write a chat template is to first
 write a short Python script that formats messages the way you want, and then convert that script into a template.