Apply suggestions from code review

Incorporating Vinay's edits

Co-authored-by: Vinay Payyapilly <vpayyapilly@newrelic.com>

src/content/docs/alerts/incident-management/Issues-and-Incident-management-and-response.mdx

@@ -130,24 +130,24 @@ The <DNT>**Issue page**</DNT> includes the following sections:
 
 The causal analysis engine identifies potential symptoms that might have triggered an alert event and suggests immediate mitigating actions to address them.
 
-Consider a scenario where a PHP application encounters a memory leak, leading to a failure in the throughput SLI and triggering an alert. Our engine investigates by moving from the service level to the APM application and then to the infrastructure container to detect the symptom. 
+Consider a scenario where a PHP application encounters a memory leak that leads to a failure in the throughput SLI and triggers an alert. The engine investigates by moving from the service level to the APM application and then to the infrastructure container to detect the symptom. 
 
 ### How does the engine work?
-The causal analysis engine uses distinct analysis categories, such as deployment events, infrastructure resource limits, and more. Each category is designed to address various potential sources of anomalies and performance issues. These categories focus on specific data types and metrics, enabling precise analysis and more accurate identification of causal relationships.
+The causal analysis engine uses distinct analysis categories such as deployment events, infrastructure resource limits, and more. Each category is designed to address various potential sources of anomalies and performance issues. Each category focuses on specific data types and metrics, enabling precise analysis and more accurate identification of causal relationships.
 
 At the moment, we’re primarily focused on APM entity causal analysis. In the near term, our plan is to include infrastructure, Browser, among other entity types. 
 
 ### Mitigating actions & visualizations
 
-For every identified potential cause, the engine offers tailored mitigation actions that guide users through the necessary steps to quickly restore services and entities to their normal operational states. We recognize that many of our customers typically rely on NRQL to analyze significant queries, hence we provide relevant visuals alongside the underlying query for each cause.
+For every identified potential cause, the engine offers tailored mitigation actions that guide users through the necessary steps to quickly restore services and entities to their normal operational states. Since many of our customers rely on NRQL to analyze significant queries, we provide relevant visuals alongside the underlying query for each cause.
 
 ### Confidence scores
 
-In certain situations, you may encounter multiple potential causes. To assist you in prioritizing which cause to examine first, we provide confidence scores. These scores are categorized as low, medium, or high confidence.
+In certain situations, you may encounter multiple potential causes. To assist you in prioritizing which cause to examine first, New Relic provides confidence scores. These scores are categorized as low, medium, or high confidence.
 
 ### NR AI generated analysis
 
-In some scenarios, our causal engine may not identify an algorithm-driven cause. However, we have insights that can be utilized with LLMs to offer you actionable steps. Customers interested in this capability must have the New Relic AI entitlement enabled.
+In some scenarios, the causal engine may not identify an algorithm-driven cause. However, the insights can be used with LLMs to offer you actionable steps. To use this capability, enable the New Relic AI entitlement.
 
 ## Postmortem [#postmortem-intro]
 
@@ -243,4 +243,4 @@ To view the issues in a text format, in the right hand corner, click <DNT>**Swit
 
 To further reduce noise or get improved incident correlation, you can change or customize your decisions. Decisions determine how incidents are grouped together.
 
-To get started, see [Decisions](/docs/new-relic-one/use-new-relic-one/new-relic-ai/get-started-decisions).
+To get started, refer to [Decisions](/docs/new-relic-one/use-new-relic-one/new-relic-ai/get-started-decisions).

src/content/docs/alerts/incident-management/response-intelligence-ai.mdx

@@ -30,4 +30,4 @@ First responders need to assess the "blast radius" to determine the severity of
 Many IT issues tend to reoccur. Knowing if an issue has happened before, why it occurred, and how it was resolved can save first responders valuable time during an incident. To support this, customers can use the widget to link their existing retrospective or postmortem documents. By leveraging retrieval augmented generation (RAG), the New Relic AI platform will index and store this information for future contextual reference. Once configured, first responders will see a summary of similar past issues, along with links to the retrospective documents for detailed analysis.
 
 ## What to check?
-First responders often need contextual guidance on immediate actions to mitigate an issue. This widget provides customized steps to help them quickly restore services to normal operational levels. Additionally, the Potential causes tab identifies likely causes through causal analysis, covering a range of possible anomalies and performance issues. For more information, refer to casual analysis.
+First responders often need contextual guidance on immediate actions to mitigate an issue. This widget provides customized steps to help them quickly restore services to normal operational levels. Additionally, the Potential causes tab identifies likely causes through causal analysis, covering a range of possible anomalies and performance issues. For more information, refer to causal analysis.