diff --git a/src/transformers/generation/logits_process.py b/src/transformers/generation/logits_process.py
index 550380c07cdee1..59492c37a34c55 100644
--- a/src/transformers/generation/logits_process.py
+++ b/src/transformers/generation/logits_process.py
@@ -547,6 +547,7 @@ class TypicalLogitsWarper(LogitsWarper):
     >>> set_seed(18)
     >>> outputs = model.generate(**inputs, do_sample=True)
     >>> print(tokenizer.batch_decode(outputs, skip_special_tokens=True)[0])
+    1, 2, 3, 4, 5, 6, 7, 8, 9 and 10
 
     >>> # With `typical_p` set, the most obvious sequence is no longer produced, which may be good for your problem
     >>> set_seed(18)
@@ -557,7 +558,7 @@ class TypicalLogitsWarper(LogitsWarper):
     1, 2, 3 and 5
 
     >>> # We can see that the token corresponding to "4" (token 934) in the second position, the most likely token
-    >>> # under default parameterization, was entirely blocked out
+    >>> # as seen with greedy decoding, was entirely blocked out
     >>> print(outputs.scores[1][0, 934])
     tensor(-inf)
     ```
@@ -1183,6 +1184,40 @@ class PrefixConstrainedLogitsProcessor(LogitsProcessor):
             arguments `inputs_ids` and the batch ID `batch_id`. It has to return a list with the allowed tokens for the
             next generation step conditioned on the previously generated tokens `inputs_ids` and the batch ID
             `batch_id`.
+
+    Examples:
+
+    ```py
+    >>> from transformers import AutoTokenizer, AutoModelForCausalLM
+
+    >>> model = AutoModelForCausalLM.from_pretrained("bigscience/bloomz-560m")
+    >>> tokenizer = AutoTokenizer.from_pretrained("bigscience/bloomz-560m")
+
+    >>> inputs = tokenizer("Alice and Bob", return_tensors="pt")
+
+    >>> # By default, it continues generating according to the model's logits
+    >>> outputs = model.generate(**inputs, max_new_tokens=5)
+    >>> print(tokenizer.batch_decode(outputs, skip_special_tokens=True)[0])
+    Alice and Bob are friends
+
+    >>> # We can contrain it with `prefix_allowed_tokens_fn` to force a certain behavior based on a prefix.
+    >>> # For instance, we can force an entire entity to be generated when its beginning is detected.
+    >>> entity =  tokenizer(" Bob Marley", return_tensors="pt").input_ids[0]  # 3 tokens
+    >>> def prefix_allowed_tokens_fn(batch_id, input_ids):
+    ...     '''
+    ...     Attempts to generate 'Bob Marley' when 'Bob' is detected.
+    ...     In this case, `batch_id` is not used, but you can set rules for each batch member.
+    ...     '''
+    ...     if input_ids[-1] == entity[0]:
+    ...         return entity[1]
+    ...     elif input_ids[-2] == entity[0] and input_ids[-1] == entity[1]:
+    ...         return entity[2]
+    ...     return list(range(tokenizer.vocab_size))  # If no match, allow all tokens
+
+    >>> outputs = model.generate(**inputs, max_new_tokens=5, prefix_allowed_tokens_fn=prefix_allowed_tokens_fn)
+    >>> print(tokenizer.batch_decode(outputs, skip_special_tokens=True)[0])
+    Alice and Bob Marley
+    ```
     """
 
     def __init__(self, prefix_allowed_tokens_fn: Callable[[int, torch.Tensor], List[int]], num_beams: int):