Apply suggestions from code review

Co-authored-by: SamyOubouaziz <[email protected]>

Author: bene2k1

tutorials/how-to-implement-rag-generativeapis/index.mdx

@@ -342,9 +342,9 @@ Streaming responses:
 You can customize the streaming behavior further, such as implementing progress indicators or more sophisticated UI elements for applications.
 
 #### Example use cases
-- Customer support: Use a custom prompt to answer customer queries effectively, making the interactions feel more personalized and engaging.
-- Research assistance: Tailor prompts to provide concise summaries or detailed explanations on specific topics, enhancing your research capabilities.
-- Content generation: Personalize prompts for creative writing, generating responses that align with specific themes or tones.
+- Customer support: use a custom prompt to answer customer queries effectively, making the interactions feel more personalized and engaging.
+- Research assistance: tailor prompts to provide concise summaries or detailed explanations on specific topics, enhancing your research capabilities.
+- Content generation: personalize prompts for creative writing, generating responses that align with specific themes or tones.
 
 ## Conclusion
 

tutorials/processing-images-structured-outputs-pixtral/index.mdx

@@ -1,9 +1,9 @@
 ---
 meta:
-  title: Processing Images and Getting Structured Outputs with Pixtral Vision Model
+  title: Processing images and getting structured outputs with Pixtral vision model
   description: Learn how to use Mistral's Pixtral vision model to automatically generate a product catalog from images.
 content:
-  h1: Processing Images and Getting Structured Outputs with Pixtral Vision Model
+  h1: Processing images and getting structured outputs with Pixtral vision model
   paragraph: Discover how to leverage Mistral's Pixtral vision model to analyze images and generate comprehensive structured outputs.
 tags: AI vision-model image-processing Pixtral Mistral structured-data
 categories:
@@ -52,7 +52,7 @@ Before we dive into using Pixtral, let's set up our Python environment and insta
 
 ## Defining the data model
 
-We'll start by defining our data model using Pydantic. This will ensure that our structured output has a consistent format and that all required fields are present.
+We'll start by defining our data model using `pydantic`. This will ensure that our structured output has a consistent format and that all required fields are present.
 
 Create a new file called `models.py` and add the following code:
 
@@ -126,7 +126,7 @@ def get_pixtral_client():
     return client
 ```
 
-Make sure to set the `SCALEWAY_API_KEY` and `SCALEWAY_INFERENCE_ENDPOINT_URL` environment variables with your actual API key from Scaleway's IAM and the appropriate endpoint URL for Scaleway Managed Inference or Generative APIs service.
+Make sure to set the `SCALEWAY_API_KEY` and `SCALEWAY_INFERENCE_ENDPOINT_URL` environment variables with your actual API key from Scaleway IAM, and the appropriate endpoint URL for Scaleway Managed Inference or Generative APIs service.
 
 ## Creating the image processor
 
@@ -220,7 +220,7 @@ This script does the following:
 2. Defines a function to process images using the Pixtral model.
 3. Creates a prompt that instructs the model on how to analyze the images and what information to extract.
 4. Sends the images and prompt to the Pixtral model and receives the generated structured data.
-5. Validates the received data against our Pydantic models.
+5. Validates the received data against our `pydantic` models.
 6. Saves the generated structured output to a JSON file.
 
 ## Running the image processor