Apply suggestions from code review

Co-authored-by: Rowena Jones <[email protected]>

Author: bene2k1

ai-data/generative-apis/concepts.mdx

@@ -22,7 +22,7 @@ A context window is the maximum amount of prompt data considered by the model to
 
 ## Function calling
 
-Function calling allows a language model (LLM) to interact with external tools or APIs, executing specific tasks based on user requests. The LLM identifies the appropriate function, extracts needed parameters, and returns the results as structured data, typically in JSON format.
+Function calling allows a large language model (LLM) to interact with external tools or APIs, executing specific tasks based on user requests. The LLM identifies the appropriate function, extracts the required parameters, and returns the results as structured data, typically in JSON format.
 
 ## Embeddings
 

ai-data/generative-apis/how-to/use-function-calling.mdx

@@ -15,7 +15,7 @@ Scaleway's Chat Completions API supports function calling as introduced by OpenA
 
 ## What is function calling?
 
-Function calling allows a language model (LLM) to interact with external tools or APIs, executing specific tasks based on user requests. The LLM identifies the appropriate function, extracts needed parameters, and returns the results as structured data, typically in JSON format. While errors can occur, custom parsers or tools like LlamaIndex and LangChain can help ensure valid results.
+Function calling allows a large language model (LLM) to interact with external tools or APIs, executing specific tasks based on user requests. The LLM identifies the appropriate function, extracts the required parameters, and returns the results as structured data, typically in JSON format. While errors can occur, custom parsers or tools like LlamaIndex and LangChain can help ensure valid results.
 
 <Macro id="requirements" />
 

ai-data/managed-inference/concepts.mdx

@@ -44,7 +44,7 @@ It demonstrates the model's ability to generalize from limited training data to
 
 ## Function calling
 
-Function calling allows a language model (LLM) to interact with external tools or APIs, executing specific tasks based on user requests. The LLM identifies the appropriate function, extracts needed parameters, and returns the results as structured data, typically in JSON format.
+Function calling allows a large language model (LLM) to interact with external tools or APIs, executing specific tasks based on user requests. The LLM identifies the appropriate function, extracts the required parameters, and returns the results as structured data, typically in JSON format.
 
 ## Hallucinations
 

ai-data/managed-inference/reference-content/function-calling-support.mdx

@@ -12,7 +12,7 @@ categories:
 
 ## What is function calling?
 
-Function calling allows a language model (LLM) to interact with external tools or APIs, executing specific tasks based on user requests. The LLM identifies the appropriate function, extracts needed parameters, and returns the results as structured data, typically in JSON format. While errors can occur, custom parsers or tools like LlamaIndex and LangChain can help ensure valid results.
+Function calling allows a large language model (LLM) to interact with external tools or APIs, executing specific tasks based on user requests. The LLM identifies the appropriate function, extracts the required parameters, and returns the results as structured data, typically in JSON format. While errors can occur, custom parsers or tools like LlamaIndex and LangChain can help ensure valid results.
 
 ## How to implement function calling in Scaleway Managed Inference?