Updating files using the deprecated :_content-type: attribute, updated to :_mod-docs-content-type:

docs/topics/about-ide-addons.adoc

@@ -4,7 +4,7 @@
 // * docs/intellij-idea-plugin-guide/master.adoc
 // * docs/eclipse-code-ready-guide/master.adoc
 
-:_content-type: CONCEPT
+:_mod-docs-content-type: CONCEPT
 [id="about-ide-addons_{context}"]
 = About the {ProductShortName} {AddonType} for {IDEName}
 

docs/topics/developer-lightspeed/con_agent-ai.adoc

@@ -2,7 +2,7 @@
 //
 // * docs/cli-guide/master.adoc
 
-:_content-type: CONCEPT
+:_mod-docs-content-type: CONCEPT
 [id="agent-ai_{context}"]
 
 = About the Agent AI
@@ -15,10 +15,10 @@ You can enable the Agent AI or the Solution Server mode to request AI-assisted c
 * Chooses a suitable sub agent for the analysis task. {mta-dl-plugin} works with the LLM to generate fix suggestions. The LLM displays the reasoning transcript and files to be changed.
 * Applies the changes to the code once you approve the updates.
 
-If you accept that the agentic AI must continue to make changes, it compiles the code and runs a partial analysis. In this iteration, the agentic AI attempts to fix diagnostic issues (if any) generated by tools that you installed in the Visual Studio Code IDE. You can review the changes and accept the agentic AI's suggestion to address these diagnostic issues. 
+If you accept that the agentic AI must continue to make changes, it compiles the code and runs a partial analysis. In this iteration, the agentic AI attempts to fix diagnostic issues (if any) generated by tools that you installed in the Visual Studio Code IDE. You can review the changes and accept the agentic AI's suggestion to address these diagnostic issues.
 
 After each iteration of applying changes to the code, the agentic AI asks if you want the agent to continue fixing more issues. When you accept, it runs another iteration of automated analysis until it has resolved all issues or it has made a maximum of two attempts to fix an issue.
 
 Agentic AI generates a new preview in each iteration when it updates the code with the suggested resolutions. The time taken by the agentic AI to complete all iterations depends on the number of new diagnostic issues that are detected in the code.
 
-:!agent-ai:
+:!agent-ai:

docs/topics/developer-lightspeed/con_dl-context.adoc

@@ -2,21 +2,21 @@
 //
 // * docs/cli-guide/master.adoc
 
-:_content-type: CONCEPT
+:_mod-docs-content-type: CONCEPT
 [id="dl-context_{context}"]
 
 [role=_abstract]
 = Describing the context
 
 :context: dl-context
 
-The context is a combination of the source code, the issue description, and solved examples: 
+The context is a combination of the source code, the issue description, and solved examples:
 
 * Description of issues detected by {ProductShortName} when you run a static code analysis for a given set of target technologies.
 
 * (Optional) The default and custom rules may contain additional information that you include which can help {mta-dl-plugin} to define the context.
 +
-* Solved examples constitute code changes from other migrations and a pattern of resolution for an issue that can be used in future. A solved example is created when a Migrator accepts a resolution in a previous analysis that results in updated code or an unfamiliar issue in a legacy application that the Migrator manually fixed. Solved examples are stored in the Solution Server. 
+* Solved examples constitute code changes from other migrations and a pattern of resolution for an issue that can be used in future. A solved example is created when a Migrator accepts a resolution in a previous analysis that results in updated code or an unfamiliar issue in a legacy application that the Migrator manually fixed. Solved examples are stored in the Solution Server.
 +
 More instances of solved examples for an issue enhance the context and improve the success metrics of rules that trigger the issue. Higher the success metrics of an issue, greater the confidence level associated with the accepted resolutions for that issue in previous analyses.
 
@@ -26,4 +26,4 @@ The improvement in the quality of migration hints results in more accurate code
 +
 This cyclical improvement of resolution pattern from the Solution Server and improved migration hints lead to more reliable code changes as you migrate applications in different migration waves.
 
-:!dl-context:
+:!dl-context:

docs/topics/developer-lightspeed/con_intro-to-developer-lightspeed.adoc

@@ -2,7 +2,7 @@
 //
 // * docs/cli-guide/master.adoc
 
-:_content-type: CONCEPT
+:_mod-docs-content-type: CONCEPT
 [id="intro-to-the-developer-lightspeed_{context}"]
 = Introduction to the {mta-dl-plugin}
 
@@ -25,7 +25,7 @@ Migrators do duplicate work by resolving issues that are repeated across applica
 
 {mta-dl-plugin} works by collecting and storing the changes in the code for a large collection of applications, finding context to generate prompts for the LLM of your choice, and by generating code resolutions produced by the LLM to address specific issues.
 
-{mta-dl-plugin} augments the manual changes made to code throughout your organization in different migration waves and creates a context for a large language model (LLM). The LLM suggests code resolutions based on the issue description, context, and previous examples of code changes to resolve issues. 
+{mta-dl-plugin} augments the manual changes made to code throughout your organization in different migration waves and creates a context for a large language model (LLM). The LLM suggests code resolutions based on the issue description, context, and previous examples of code changes to resolve issues.
 
 {mta-dl-plugin} uses Retrieval Augmented Generation for context-based resolutions of issues in code. By using RAG, {mta-dl-plugin} improves the context shared with the LLM to generate more accurate suggestions to fix the issue in the code. The context allows the LLM to "reason" and generate suggestions for issues detected in the code. This mechanism helps to overcome the limited context size in LLMs that prevents them from analyzing the entire source code of an application.
 
@@ -42,4 +42,4 @@ For more information about the support scope of Red Hat Technology Preview featu
 ====
 :!FeatureName:
 
-:!intro-to-the-developer-lightspeed:
+:!intro-to-the-developer-lightspeed:

docs/topics/developer-lightspeed/con_solution_server.adoc

@@ -2,14 +2,14 @@
 //
 // * docs/cli-guide/master.adoc
 
-:_content-type: CONCEPT
+:_mod-docs-content-type: CONCEPT
 [id="solution-server_{context}"]
 
 = About the Solution Server
 :context: solution-server
 
 [role=_abstract]
-Solution Server is a component that allows {mta-dl-plugin} to build a collective memory of source code changes from all analyses performed in an organization. This helps you to use the recurring patterns of solutions for issues that repeat in many applications. You can opt to use the Solution Server to request AI-assisted code resolutions. 
+Solution Server is a component that allows {mta-dl-plugin} to build a collective memory of source code changes from all analyses performed in an organization. This helps you to use the recurring patterns of solutions for issues that repeat in many applications. You can opt to use the Solution Server to request AI-assisted code resolutions.
 
 The Solution Server augments previous patterns of how source code changed to resolve issues (also called solved examples) that were similar to those in the current file, and suggests a resolution that has a higher confidence level derived from previous solutions. After you accept a suggested code fix, the Solution Server works with the large language model (LLM) to improve the hints about the issue that becomes part of the context. An improved context enables the LLM to generate more reliable code fix suggestions in future cases.
 
@@ -18,7 +18,7 @@ The Solution Server delivers two primary benefits to users:
 * *Contextual Hints*: It surfaces examples of past migration solutions — including successful user modifications and accepted fixes — offering actionable hints for difficult or previously unsolved migration problems.
 * *Migration Success Metrics*: It exposes detailed success metrics for each migration rule, derived from real-world usage data. These metrics can be used by IDEs or automation tools to present users with a “confidence level” or likelihood of {mta-dl-plugin} successfully migrating a given code segment.
 
-When you use the Solution Server, you can view a diff of the updated portions of the code and the original source code to do a manual review. In a manual review of the suggested AI resolutions, you can accept, reject, or edit the suggested code changes. 
+When you use the Solution Server, you can view a diff of the updated portions of the code and the original source code to do a manual review. In a manual review of the suggested AI resolutions, you can accept, reject, or edit the suggested code changes.
 
 :FeatureName: Solution Server
 [IMPORTANT]
@@ -43,4 +43,4 @@ For more information about the support scope of Red Hat Technology Preview featu
 
 |===
 
-:!solution-server:
+:!solution-server:

docs/topics/developer-lightspeed/ref_dl-benefits.adoc

@@ -2,20 +2,20 @@
 //
 // * docs/cli-guide/master.adoc
 
-:_content-type: REFERENCE
+:_mod-docs-content-type: REFERENCE
 
 [id="benefits-using-developer-lightspeed_{context}"]
 = Benefits of using {mta-dl-plugin}
 
 :context: dl-benefits
 
 [role=_abstract]
-{mta-dl-full} offers options to use large language models of your choice, LLM configuration control, and a platform engineering approach to standardize AI-assisted refactoring efforts for large codebases in your organization.  
+{mta-dl-full} offers options to use large language models of your choice, LLM configuration control, and a platform engineering approach to standardize AI-assisted refactoring efforts for large codebases in your organization.
 
 * *Model agnostic* - {mta-dl-plugin} follows a "Bring Your Own Model" approach, allowing your organization to use a preferred LLM.
 * *Iterative refinement* - {mta-dl-plugin} can include an agent that iterates through the source code to run a series of automated analyses that resolves both the code base and diagnostic issues.
 * *Contextual code generation* - By leveraging AI for static code analysis, {mta-dl-plugin} breaks down complex problems into more manageable ones, providing the LLM with focused context to generate meaningful results. This helps overcome the limited context size of LLMs when dealing with large codebases.
 * *No fine tuning* - You also do not need to fine tune your model with a suitable data set for analysis which leaves you free to use and switch LLM models to respond to your requirements.
 * *Learning and Improvement* - As more parts of a codebase are migrated with {mta-dl-plugin}, it can use RAG to learn from the available data and provide better recommendations in subsequent application analysis.
 
-:!dl-benefits:
+:!dl-benefits:

docs/topics/important-links.adoc

@@ -3,7 +3,7 @@
 // * docs/cli-guide/master.adoc
 // * docs/maven-guide/master.adoc
 
-:_content-type: REFERENCE
+:_mod-docs-content-type: REFERENCE
 [id="important-links_{context}"]
 = {ProductName} development resources
 

method-discover.adoc

@@ -2,7 +2,7 @@
 //
 // * docs/getting-started-guide/master.adoc
 
-:_content-type: CONCEPT
+:_mod-docs-content-type: CONCEPT
 [id="method-discover_{context}"]
 = Discover phase
 
@@ -13,4 +13,4 @@ The _Discover_ phase is when you gather all stakeholders and decision-makers tog
 
 In this phase, you explore technologies and discuss potential approaches. Identify the existing pain points, concerns, requirements, and some potential challenges. Define the high-level business priorities and scope of the assessment. Determine in what ways you want to modernize your application development and delivery to allow you to innovate more rapidly.
 
-Typically, this can be covered in a day in a workshop with Red Hat experts.
+Typically, this can be covered in a day in a workshop with Red Hat experts.