Document use of SkipJsonSchema for omitting fields

docs/concepts/fields.md

@@ -86,6 +86,24 @@ print(date_range.model_dump_json())
 #> {"start_date":"2021-01-01","end_date":"2021-01-30"}
 ```
 
+## Omitting fields from schema sent to the language model
+
+In some cases, you may wish to have the language model ignore certain fields in your model. You can do this by using Pydantic's `SkipJsonSchema` annotation. This omits a field from the JSON schema emitted by Pydantic (which `instructor` uses for constructing its prompts and tool definitions). For example:
+
+```py
+from pydantic import BaseModel
+from pydantic.json_schema import SkipJsonSchema
+
+class Response(BaseModel):
+    question: str
+    answer: str
+    private_field: SkipJsonSchema[str | None] = None
+
+assert "private_field" not in Response.model_json_schema()["properties"]
+```
+
+Note that because the language model will never return a value for `private_field`, you'll need a default value (this can be a generator via a declared Pydantic `Field`). 
+
 ## Customizing JSON Schema
 
 There are some fields that are exclusively used to customise the generated JSON Schema:

docs/concepts/models.md

@@ -30,7 +30,7 @@ Here all docstrings, types, and field annotations will be used to generate the p
 
 ## Optional Values
 
-If we use `Optional` and `default`, they will be considered not required when sent to the language model
+If we use `Optional` and `default`, they will be considered not required when sent to the language model.
 
 ```python
 from pydantic import BaseModel, Field
@@ -43,6 +43,8 @@ class User(BaseModel):
     email: Optional[str] = Field(description="The email of the user.", default=None)
 ```
 
+Note that fields can also be omitted entirely from being sent to the language model by using Pydantic's `SkipJsonSchema` annotation. See [Fields](fields.md#omitting-fields-from-schema-sent-to-the-language-model) for additional details.
+
 ## Dynamic model creation
 
 There are some occasions where it is desirable to create a model using runtime information to specify the fields. For this, Pydantic provides the create_model function to allow models to be created on the fly: