Fixing ryan's comments

Author: jfilcik

articles/ai-services/content-understanding/video/overview.md

@@ -32,7 +32,7 @@ This format can drop straight into a vector store to enable an agent or RAG work
 From there you can **customize the analyzer** for more fine-grained control of the output. You can define custom fields, segments, or enable face identification. Customization allows you to use the full power of generative models to extract deep insights from the visual and audio details of the video. For example, customization allows you to:
 
 - Identify what products and brands are seen or mentioned in the video.
-- Segment a basketball video by different plays such as `offensive play`, `defensive play`, `free throw`.
+- Segment a news broadcast into chapters based on the topics or news stories discussed.
 - Use face identification to label speakers as executives, for example, `CEO John Doe`, `CFO Jane Smith`.
 
 ## Why use Content Understanding for video?
@@ -46,7 +46,7 @@ Content understanding for video has broad potential uses. For example, you can c
 
 ## Prebuilt video analyzer example
 
-With the prebuilt video analyzer, you can upload a video and get an immediately usable knowledge asset. The service packages every clip into both richly formatted Markdown and JSON. This process allows your search index or chat agent to ingest without custom glue code.
+With the prebuilt video analyzer (prebuilt-videoAnalyzer), you can upload a video and get an immediately usable knowledge asset. The service packages every clip into both richly formatted Markdown and JSON. This process allows your search index or chat agent to ingest without custom glue code.
 Calling prebuilt-video with no custom schema returns a document like the following (abridged) example:
 
 ```markdown
@@ -102,9 +102,9 @@ The service operates in two stages. The first stage, content extraction, involve
 
 The first pass is all about extracting a first set of details—who's speaking, where are the cuts, and which faces recur. It creates a solid metadata backbone that later steps can reason over.
 
-* **Transcription:** Converts conversational audio into searchable and analyzable text-based transcripts in WebVTT format. Sentence-level and word-level timestamps are available upon request. Content Understanding supports the full set of Azure AI Speech speech-to-text languages. For languages with Fast transcriptions support and for files ≤ 300 MB and/or ≤ 2 hours, transcription time is reduced substantially. Additionally, the following transcription details are important to consider:
+* **Transcription:** Converts conversational audio into searchable and analyzable text-based transcripts in WebVTT format. Sentence-level timestamps are available upon request. Content Understanding supports the full set of Azure AI Speech speech-to-text languages. For languages with Fast transcriptions support and for files ≤ 300 MB and/or ≤ 2 hours, transcription time is reduced substantially. Additionally, the following transcription details are important to consider:
   * **Diarization:** Distinguishes between speakers in a conversation in the output, attributing parts of the transcript to specific speakers.
-  * **Multilingual transcription:** Generates multilingual transcripts, applying language/locale per phrase. Deviating from language detection this feature is enabled when no language/locale is specified or language is set to `auto`.
+  * **Multilingual transcription:** Generates multilingual transcripts. Language/locale is applied per phrase in the transcriptPhrases output when `returnDetails=true` is set. Deviating from language detection this feature is enabled when no language/locale is specified or language is set to `auto`.
 
     > [!WARNING]
     > When multilingual transcription is used, a file with an unsupported locale still produces a result. This result is based on the closest locale but most likely not correct.
@@ -113,8 +113,6 @@ The first pass is all about extracting a first set of details—who's speaking,
 * **Shot detection:** Identifies segments of the video aligned with shot boundaries where possible, allowing for precise editing and repackaging of content with breaks exactly on shot boundaries.
 * **Key frame extraction:** Extracts key frames from videos to represent each shot completely, ensuring each shot has enough key frames to enable field extraction to work effectively.
 
-
-
 ## Field extraction and segmentation
 
 Next, the generative model layers meaning—tagging scenes, summarizing actions, and slicing footage into segments per your request. This action is where prompts turn into structured data.
@@ -127,7 +125,7 @@ Shape the output to match your business vocabulary. Use a `fieldSchema` object w
 
 * **Media asset management:**
 
-  * **Shot type:** Helps editors and producers organize content, simplifying editing, and understanding the visual language of the video. Useful for metadata tagging and quicker scene retrieval.
+  * **Video Category:** Helps editors and producers organize content, by classifying it as News, Sports, Interview, Documentary, Advertisement, etc. Useful for metadata tagging and quicker content filtering and retrieval.
   * **Color scheme:** Conveys mood and atmosphere, essential for narrative consistency and viewer engagement. Identifying color themes helps in finding matching clips for accelerated video editing.
 
 * **Advertising:**
@@ -238,7 +236,7 @@ Specific limitations of video processing to keep in mind:
 
 * **Frame sampling (\~ 1 FPS)**: The analyzer inspects about one frame per second. Rapid motions or single-frame events may be missed.
 * **Frame resolution (512 × 512 px)**: Sampled frames are resized to 512 pixels square. Small text or distant objects can be lost.
-* **Speech**: Only spoken words are transcribed. Music, sound effects, and ambient noise are ignored. Specific of supported locals are document.
+* **Speech**: Only spoken words are transcribed. Music, sound effects, and ambient noise are ignored.
 
 ## Input requirements