Improve AI concepts doc

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

@@ -3,7 +3,7 @@
 
 The integration of function support in AI models, permits the model to request the execution of client-side functions, thereby accessing necessary information or performing tasks dynamically as required.
 
-image::function-calling-basic-flow2.jpg[Function calling, width=700, align="center"]
+image::function-calling-basic-flow.jpg[Function calling, width=700, align="center"]
 
 Spring AI currently supports Function invocation for the following AI Models
 

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

@@ -26,7 +26,7 @@ Portable `Vector Store API` across multiple providers, including a novel `SQL-li
 
 `Function calling`. Spring AI makes it easy to have the AI model invoke your POJO `java.util.Function` object.
 
-image::function-calling-basic-flow2.jpg[Function calling, width=500, align="center"]
+image::function-calling-basic-flow.jpg[Function calling, width=500, align="center"]
 
 Check the Spring AI xref::api/functions.adoc[Function Calling] documentation.
 

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

@@ -144,7 +144,7 @@ Also, asking "`for JSON`" as part of the prompt is not 100% accurate.
 
 This intricacy has led to the emergence of a specialized field involving the creation of prompts to yield the intended output, followed by converting the resulting simple string into a usable data structure for application integration.
 
-image::structured-output-architecture.jpg[Structured Output Converter Architecture, width=900, align="center"]
+image::structured-output-architecture.jpg[Structured Output Converter Architecture, width=800, align="center"]
 
 The xref:api/structured-output-converter.adoc#_structuredoutputconverter[Structured output conversion] employs meticulously crafted prompts, often necessitating multiple interactions with the model to achieve the desired formatting.
 
@@ -169,7 +169,7 @@ The Spring AI library helps you implement solutions based on the "`stuffing the
 Spring AI greatly simplifies code you need to write to support xref:api/functions.adoc[function calling].
 
 [[concept-rag]]
-== Retrieval Augmented Generation
+=== Retrieval Augmented Generation
 
 A technique termed Retrieval Augmented Generation (RAG) has emerged to address the challenge of incorporating relevant data into prompts for accurate AI model responses.
 
@@ -194,12 +194,10 @@ image::spring-ai-rag.jpg[Spring AI RAG, width=1000, align="center"]
 * The xref::api/etl-pipeline.adoc[ETL pipeline] provides further information about orchestrating the flow of extracting data from the data sources and stor it in a structured vector store, ensuring data is in the optimal format for retrieval by the AI model.
 * The xref::api/chatclient.adoc#_retrieval_augmented_generation[ChatClient - RAG] explains how to use the `QuestionAnswerAdvisor` advisor to enable the RAG capability to your application.
 
-== Function Calling
+=== Function Calling
 
 Large Language Models (LLMs) are frozen after training, leading to stale knowledge and they are unable to access or modify external data.
 
-image::function-calling-basic-flow2.jpg[Function calling, width=700, align="center"]
-
 The xref::api/functions.adoc[Function Calling] mechanism addresses these shortcomings.
 It allows you to register your own functions to connect the large language models to the APIs of external systems.
 These systems can provide LLMs with real-time data and perform data processing actions on their behalf.
@@ -209,6 +207,19 @@ It handles the function invocation conversation for you.
 You can provide your function as a `@Bean` and then provide the bean name of the function in your prompt options to activate that function.
 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 along with a function definition information.
+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).
+* (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 (4).
+Model can perform multiple function calls to retrieve all the information it needs.
+* (5) once all information needed is acquired, the Model will generate a response.
+
+Follow the xref::api/functions.adoc[Function Calling] documentation for further information on how to use this feature with different AI models.
+
+[[concept-evaluating-ai-responses]]
 == Evaluating AI responses
 
 Effectively evaluating the output of an AI system in response to user requests is very important to ensuring the accuracy and usefulness of the final application.