Correct some grammar typos with 'latter'

spring-ai-docs/src/main/antora/modules/ROOT/pages/api/embeddings/onnx.adoc

@@ -24,7 +24,7 @@ source ./venv/bin/activate
 (venv) optimum-cli export onnx --model sentence-transformers/all-MiniLM-L6-v2 onnx-output-folder
 ----
 
-The snippet exports the https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2[sentence-transformers/all-MiniLM-L6-v2] transformer into the `onnx-output-folder` folder. Later includes the `tokenizer.json` and `model.onnx` files used by the embedding model.
+The snippet exports the https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2[sentence-transformers/all-MiniLM-L6-v2] transformer into the `onnx-output-folder` folder. The latter includes the `tokenizer.json` and `model.onnx` files used by the embedding model.
 
 In place of the all-MiniLM-L6-v2 you can pick any huggingface transformer identifier or provide direct file path.
 

spring-ai-docs/src/main/antora/modules/ROOT/pages/api/structured-output-converter.adoc

@@ -3,7 +3,7 @@
 = Structured Output Converter
 
 NOTE: As of 02.05.2024 the old `OutputParser`, `BeanOutputParser`, `ListOutputParser` and `MapOutputParser` classes are deprecated in favor of the new `StructuredOutputConverter`, `BeanOutputConverter`, `ListOutputConverter` and `MapOutputConverter` implementations.
-Later are drop-in replacements for the former ones and provide the same functionality.   The reason for the change was primarily naming, as there isn't any parsing being done, but also have aligned with the Spring `org.springframework.core.convert.converter` package brining in some improved functionality.
+the latter are drop-in replacements for the former ones and provide the same functionality.   The reason for the change was primarily naming, as there isn't any parsing being done, but also have aligned with the Spring `org.springframework.core.convert.converter` package brining in some improved functionality.
 
 The ability of LLMs to produce structured outputs is important for downstream applications that rely on reliably parsing output values.
 Developers want to quickly turn results from an AI model into data types, such as JSON, XML or Java Classes, that can be passed to other application functions and methods.

spring-ai-docs/src/main/antora/modules/ROOT/pages/concepts.adoc

@@ -189,7 +189,7 @@ Additionally, you can define and reference multiple functions in a single prompt
 image::function-calling-basic-flow.jpg[Function calling, width=700, align="center"]
 
 * (1) perform a chat request sending along function definition information.
-The later provides the `name`, `description` (e.g. explaining when the Model should call the function), and `input parameters` (e.g. the function's input parameters schema).
+The latter provides the `name`, `description` (e.g. explaining when the Model should call the function), and `input parameters` (e.g. the function's input parameters schema).
 * (2) when the Model decides to call the function, it will call the function with the input parameters and return the output to the model.
 * (3) Spring AI handles this conversation for you.
 It dispatches the function call to the appropriate function and returns the result to the model.