Update 2024-10-28-Testing-GenerativeAI-Chatbots.md

Implemented review comments provided by Sonali

_posts/2024-10-28-Testing-GenerativeAI-Chatbots.md

@@ -51,7 +51,7 @@ Hence GenAI model testing require sophisticated techniques such as probabilistic
 
 **4. Lack of Standardised Evaluation Metrics:** As AI technology is quite new, existing evaluation metrics may not fully capture the quality of the user experience. Establishing appropriate and comprehensive evaluation criteria is an ongoing challenge. Developing standardised metrics, that would accurately reflect the chatbot's performance and user satisfaction, remains a significant hurdle in GenAI model testing. 
 
-**5. Continuous learning and Evolution:** AI systems are designed to continuously learn from user interactions and evolve over time. This dynamic nature poses a significant challenge for testing, as it requires ongoing monitoring and testing of these models, to ensure consistent performance; and to identify and address any unintended issues that arise from continuous learning.
+**5. Continuous learning and Evolution:** AI systems are designed to continuously learn from user interactions and evolve over time. This dynamic nature poses a significant challenge for testing. It requires ongoing monitoring and testing of these models to ensure consistent performance and to identify any unintended issues that may arise from continuous learning.
 
 Despite all these challenges, regular monitoring, combined with robust testing strategies can help ensure the accuracy, reliability and user satisfaction of their GenAI chatbot model.  
 
@@ -79,6 +79,7 @@ This involves testing the model on various devices, operating systems, browsers,
 Testing GenAI chatbot models involves evaluating multiple aspects, such as the quality, relevance, coherence and ethical considerations of the outputs generated by these models. Here is a structured approach to testing GenAI models, along with some tools and best practices: 
 
 **Define Clear Evaluation Metrics** Establish specific metrics to evaluate the performance of the model. Common metrics include: 
+
 - Accuracy: How correct the outputs are based on expected results. 
 - Relevance: The relevance of the generated content to the input prompt. 
 - Coherence: Logical consistency and fluency of the generated text.