Update fine-tuning-considerations.md

Author: ssalgadodev

articles/ai-services/openai/concepts/fine-tuning-considerations.md

@@ -33,7 +33,7 @@ Since fine-tuned models need fewer examples in the prompt, they process requests
 
 Fine-tuning applies the extensive pretraining of language models and hones their capabilities for specific applications, making them more efficient and effective for targeted use cases. 
 
-Fine-tuning smaller models can achieve performance levels comparable to larger, more expensive models for specific tasks. This approach reduces computational costs and increases speed, making it a cost-effective scalable solution for deploying Al in resource-constrained environments. 
+Fine-tuning smaller models can achieve performance levels comparable to larger, more expensive models for specific tasks. This approach reduces computational costs and increases speed, making it a cost-effective scalable solution for deploying AI in resource-constrained environments. 
 
 ## When to fine-tune
 
@@ -57,7 +57,7 @@ Fine-tuning is suited for times when you have a small amount of data and want to
 
 Azure AI Foundry offers multiple types of fine -tuning techniques:
 
-* **Supervised fine-tuning**: This allows you to provide custom data (prompt/completion or conversational chat, depending on the model) to teach the base model new skills. This process involves further training the model on a high-quality labeled dataset, where each data point is associated with the correct output or answer. The goal is to enhance the model's performance on a particular task by adjusting its parameters based on the labelled data. This technique works best when there are finite ways of solving a problem and you want to teach the model a particular task and improve its accuracy and conciseness.
+* **Supervised fine-tuning**: This allows you to provide custom data (prompt/completion or conversational chat, depending on the model) to teach the base model new skills. This process involves further training the model on a high-quality labeled dataset, where each data point is associated with the correct output or answer. The goal is to enhance the model's performance on a particular task by adjusting its parameters based on the labeled data. This technique works best when there are finite ways of solving a problem and you want to teach the model a particular task and improve its accuracy and conciseness.
 
 * **Reinforcement fine-tuning**: This is a model customization technique, beneficial for optimizing model behavior in highly complex or dynamic environments, enabling the model to learn and adapt through iterative feedback and decision-making. For example, financial services providers can optimize the model for faster, more accurate risk assessments or personalized investment advice. In healthcare and pharmaceuticals, o3-mini can be tailored to accelerate drug discovery, enabling more efficient data analysis, hypothesis generation, and identification of promising compounds. RFT is a great way to fine-tune when there are infinite or high number of ways to solve a problem. The grader rewards the model incrementally and makes reasoning better.