diff --git a/docs/source/en/llm_tutorial.md b/docs/source/en/llm_tutorial.md
index 0be71233a20a88..bb42fb4633bf69 100644
--- a/docs/source/en/llm_tutorial.md
+++ b/docs/source/en/llm_tutorial.md
@@ -74,14 +74,13 @@ If you're interested in basic LLM usage, our high-level [`Pipeline`](pipeline_tu
 
 </Tip>
 
-<!-- TODO: update example to llama 2 (or a newer popular baseline) when it becomes ungated -->
 First, you need to load the model.
 
 ```py
 >>> from transformers import AutoModelForCausalLM
 
 >>> model = AutoModelForCausalLM.from_pretrained(
-...     "openlm-research/open_llama_7b", device_map="auto", load_in_4bit=True
+...     "mistralai/Mistral-7B-v0.1", device_map="auto", load_in_4bit=True
 ... )
 ```
 
@@ -97,18 +96,31 @@ Next, you need to preprocess your text input with a [tokenizer](tokenizer_summar
 ```py
 >>> from transformers import AutoTokenizer
 
->>> tokenizer = AutoTokenizer.from_pretrained("openlm-research/open_llama_7b")
+>>> tokenizer = AutoTokenizer.from_pretrained("mistralai/Mistral-7B-v0.1", padding_side="left")
 >>> model_inputs = tokenizer(["A list of colors: red, blue"], return_tensors="pt").to("cuda")
 ```
 
 The `model_inputs` variable holds the tokenized text input, as well as the attention mask. While [`~generation.GenerationMixin.generate`] does its best effort to infer the attention mask when it is not passed, we recommend passing it whenever possible for optimal results.
 
-Finally, call the [`~generation.GenerationMixin.generate`] method to returns the generated tokens, which should be converted to text before printing.
+After tokenizing the inputs, you can call the [`~generation.GenerationMixin.generate`] method to returns the generated tokens. The generated tokens then should be converted to text before printing.
 
 ```py
 >>> generated_ids = model.generate(**model_inputs)
 >>> tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
-'A list of colors: red, blue, green, yellow, black, white, and brown'
+'A list of colors: red, blue, green, yellow, orange, purple, pink,'
+```
+
+Finally, you don't need to do it one sequence at a time! You can batch your inputs, which will greatly improve the throughput at a small latency and memory cost. All you need to do is to make sure you pad your inputs properly (more on that below).
+
+```py
+>>> tokenizer.pad_token = tokenizer.eos_token  # Most LLMs don't have a pad token by default
+>>> model_inputs = tokenizer(
+...     ["A list of colors: red, blue", "Portugal is"], return_tensors="pt", padding=True
+... ).to("cuda")
+>>> generated_ids = model.generate(**model_inputs)
+>>> tokenizer.batch_decode(generated_ids, skip_special_tokens=True)
+['A list of colors: red, blue, green, yellow, orange, purple, pink,',
+'Portugal is a country in southwestern Europe, on the Iber']
 ```
 
 And that's it! In a few lines of code, you can harness the power of an LLM.
@@ -121,10 +133,10 @@ There are many [generation strategies](generation_strategies), and sometimes the
 ```py
 >>> from transformers import AutoModelForCausalLM, AutoTokenizer
 
->>> tokenizer = AutoTokenizer.from_pretrained("openlm-research/open_llama_7b")
->>> tokenizer.pad_token = tokenizer.eos_token  # Llama has no pad token by default
+>>> tokenizer = AutoTokenizer.from_pretrained("mistralai/Mistral-7B-v0.1")
+>>> tokenizer.pad_token = tokenizer.eos_token  # Most LLMs don't have a pad token by default
 >>> model = AutoModelForCausalLM.from_pretrained(
-...     "openlm-research/open_llama_7b", device_map="auto", load_in_4bit=True
+...     "mistralai/Mistral-7B-v0.1", device_map="auto", load_in_4bit=True
 ... )
 ```
 
@@ -154,7 +166,7 @@ By default, and unless specified in the [`~generation.GenerationConfig`] file, `
 ```py
 >>> # Set seed or reproducibility -- you don't need this unless you want full reproducibility
 >>> from transformers import set_seed
->>> set_seed(0)
+>>> set_seed(42)
 
 >>> model_inputs = tokenizer(["I am a cat."], return_tensors="pt").to("cuda")
 
@@ -166,7 +178,7 @@ By default, and unless specified in the [`~generation.GenerationConfig`] file, `
 >>> # With sampling, the output becomes more creative!
 >>> generated_ids = model.generate(**model_inputs, do_sample=True)
 >>> tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
-'I am a cat.\nI just need to be. I am always.\nEvery time'
+'I am a cat.  Specifically, I am an indoor-only cat.  I'
 ```
 
 ### Wrong padding side
@@ -175,17 +187,17 @@ LLMs are [decoder-only](https://huggingface.co/learn/nlp-course/chapter1/6?fw=pt
 
 ```py
 >>> # The tokenizer initialized above has right-padding active by default: the 1st sequence,
->>> # which is shorter, has padding on the right side. Generation fails.
+>>> # which is shorter, has padding on the right side. Generation fails to capture the logic.
 >>> model_inputs = tokenizer(
 ...     ["1, 2, 3", "A, B, C, D, E"], padding=True, return_tensors="pt"
 ... ).to("cuda")
 >>> generated_ids = model.generate(**model_inputs)
->>> tokenizer.batch_decode(generated_ids[0], skip_special_tokens=True)[0]
-''
+>>> tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
+'1, 2, 33333333333'
 
 >>> # With left-padding, it works as expected!
->>> tokenizer = AutoTokenizer.from_pretrained("openlm-research/open_llama_7b", padding_side="left")
->>> tokenizer.pad_token = tokenizer.eos_token  # Llama has no pad token by default
+>>> tokenizer = AutoTokenizer.from_pretrained("mistralai/Mistral-7B-v0.1", padding_side="left")
+>>> tokenizer.pad_token = tokenizer.eos_token  # Most LLMs don't have a pad token by default
 >>> model_inputs = tokenizer(
 ...     ["1, 2, 3", "A, B, C, D, E"], padding=True, return_tensors="pt"
 ... ).to("cuda")
@@ -194,26 +206,61 @@ LLMs are [decoder-only](https://huggingface.co/learn/nlp-course/chapter1/6?fw=pt
 '1, 2, 3, 4, 5, 6,'
 ```
 
-<!-- TODO: when the prompting guide is ready, mention the importance of setting the right prompt in this section -->
+### Wrong prompt
+
+Some models and tasks expect a certain input prompt format to work properly. When this format is not applied, you will get a silent performance degradation: the model kinda works, but not as well as if you were following the expected prompt. More information about prompting, including which models and tasks need to be careful, is available in this [guide](tasks/prompting). Let's see an example with a chat LLM, which makes use of [chat templating](chat_templating):
+
+```python
+>>> tokenizer = AutoTokenizer.from_pretrained("HuggingFaceH4/zephyr-7b-alpha")
+>>> model = AutoModelForCausalLM.from_pretrained(
+...     "HuggingFaceH4/zephyr-7b-alpha", device_map="auto", load_in_4bit=True
+... )
+>>> set_seed(0)
+>>> prompt = """How many helicopters can a human eat in one sitting? Reply as a thug."""
+>>> model_inputs = tokenizer([prompt], return_tensors="pt").to("cuda")
+>>> input_length = model_inputs.input_ids.shape[1]
+>>> generated_ids = model.generate(**model_inputs, max_new_tokens=20)
+>>> print(tokenizer.batch_decode(generated_ids[:, input_length:], skip_special_tokens=True)[0])
+"I'm not a thug, but i can tell you that a human cannot eat"
+>>> # Oh no, it did not follow our instruction to reply as a thug! Let's see what happens when we write
+>>> # a better prompt and use the right template for this model (through `tokenizer.apply_chat_template`)
+
+>>> set_seed(0)
+>>> messages = [
+...     {
+...         "role": "system",
+...         "content": "You are a friendly chatbot who always responds in the style of a thug",
+...     },
+...     {"role": "user", "content": "How many helicopters can a human eat in one sitting?"},
+... ]
+>>> model_inputs = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt").to("cuda")
+>>> input_length = model_inputs.shape[1]
+>>> generated_ids = model.generate(model_inputs, do_sample=True, max_new_tokens=20)
+>>> print(tokenizer.batch_decode(generated_ids[:, input_length:], skip_special_tokens=True)[0])
+'None, you thug. How bout you try to focus on more useful questions?'
+>>> # As we can see, it followed a proper thug style 😎
+```
 
 ## Further resources
 
 While the autoregressive generation process is relatively straightforward, making the most out of your LLM can be a challenging endeavor because there are many moving parts. For your next steps to help you dive deeper into LLM usage and understanding:
 
-<!-- TODO: complete with new guides -->
 ### Advanced generate usage
 
 1. [Guide](generation_strategies) on how to control different generation methods, how to set up the generation configuration file, and how to stream the output;
-2. API reference on [`~generation.GenerationConfig`], [`~generation.GenerationMixin.generate`], and [generate-related classes](internal/generation_utils).
+2. [Guide](chat_templating) on the prompt template for chat LLMs;
+3. [Guide](tasks/prompting) on to get the most of prompt design;
+4. API reference on [`~generation.GenerationConfig`], [`~generation.GenerationMixin.generate`], and [generate-related classes](internal/generation_utils).
 
 ### LLM leaderboards
 
 1. [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard), which focuses on the quality of the open-source models;
 2. [Open LLM-Perf Leaderboard](https://huggingface.co/spaces/optimum/llm-perf-leaderboard), which focuses on LLM throughput.
 
-### Latency and throughput
+### Latency, throughput and memory utilization
 
-1. [Guide](main_classes/quantization) on dynamic quantization, which shows you how to drastically reduce your memory requirements.
+1. [Guide](llm_tutorial_optimization) on how to optimize LLMs for speed and memory;
+2. [Guide](main_classes/quantization) on quantization such as bitsandbytes and autogptq, which shows you how to drastically reduce your memory requirements.
 
 ### Related libraries