Documentation sprint chunk (pydantic#8646)

docs/concepts/performance.md

@@ -7,7 +7,7 @@ In most cases Pydantic won't be your bottle neck, only follow this if you're sur
 On `model_validate(json.loads(...))`, the JSON is parsed in Python, then converted to a dict, then it's validated internally.
 On the other hand, `model_validate_json()` already performs the validation internally.
 
-There are a few cases where `model_validate(json.loads(...))` may be faster. Specifically, when using a `'before'` validator
+There are a few cases where `model_validate(json.loads(...))` may be faster. Specifically, when using a `'before'` or `'wrap'` validator
 on a model, validation may be faster with the two step method. You can read more about these special cases in
 [this discussion](https://github.com/pydantic/pydantic/discussions/6388#discussioncomment-8193105).
 

docs/concepts/validators.md

@@ -754,3 +754,38 @@ with init_context({'multiplier': 3}):
 print(Model(my_number=2))
 #> my_number=2
 ```
+
+## Reusing Validators
+
+Occasionally, you will want to use the same validator on multiple fields/models (e.g. to normalize some input data).
+The "naive" approach would be to write a separate function, then call it from multiple decorators.
+Obviously, this entails a lot of repetition and boiler plate code.
+The following approach demonstrates how you can reuse a validator so that redundancy is minimized and the models become again almost declarative.
+
+```py
+from pydantic import BaseModel, field_validator
+
+
+def normalize(name: str) -> str:
+    return ' '.join((word.capitalize()) for word in name.split(' '))
+
+
+class Producer(BaseModel):
+    name: str
+
+    _normalize_name = field_validator('name')(normalize)
+
+
+class Consumer(BaseModel):
+    name: str
+
+    _normalize_name = field_validator('name')(normalize)
+
+
+jane_doe = Producer(name='JaNe DOE')
+print(repr(jane_doe))
+#> Producer(name='Jane Doe')
+john_doe = Consumer(name='joHN dOe')
+print(repr(john_doe))
+#> Consumer(name='John Doe')
+```