Azure AI Language can be used to identify documents containing sensitive information through its Personally Identifiable Information (PII) detection feature, which uses Named Entity Recognition (NER) to detect and categorize data like names, addresses, phone numbers, and financial details within unstructured text and native documents. This feature classifies sensitive data into predefined categories and can also redact the identified information, replacing it with asterisks to protect privacy.
Reference:
https://learn.microsoft.com/en-us/azure/ai-services/language-service/personally-identifiable-information/overview

Correct:
* AI systems should be secure and respect privacy.
* AI systems should treat people fairly.
Incorrect:
* AI systems should be in the public domain.
* AI systems should make personal details accessible.
* AI systems should NOT reveal the details of their design.
* AI systems should protect the interests of developers.
* AI systems should use black-box models.
Note:
Responsible AI principles
*-> Fairness: AI systems should treat all people fairly.
AI systems should treat everyone equally and provide the same recommendations to all individuals. Fairness in AI systems prevents discrimination based on personal characteristics.
* Reliability and safety: AI systems should perform reliably and safely.
*-> Privacy and security: AI systems should be secure and respect privacy.
* Inclusiveness: AI systems should empower everyone and engage people.
* Etc.
Reference:
https://learn.microsoft.com/en-us/azure/cloud-adoption-framework/strategy/responsible-ai

The Max Completion Tokens (or max_tokens / max_output_tokens in the API) parameter is exactly the tool you use to enforce a fixed length on AI-generated responses and keep costs predictable.
How the Parameter Works
What it does: It sets a strict upper bound on the number of output tokens the model can generate for your completion. Once this number is reached, the model will stop generating (cut off) regardless of whether it finished its thought.
What it doesn't do: It does not set a minimum length or force the model to write exactly that amount. It only acts as a ceiling.
Reference:
https://learn.microsoft.com/en-us/answers/questions/5828812/reasoning-models-like-5-1-fails-with-500-error-cod

For this scenario, you should use the Speech-to-Text Batch Transcription feature in Azure AI Speech. It is specifically built to asynchronously transcribe large volumes of prerecorded audio files from Azure Blob Storage concurrently, reducing your overall turnaround time.
To address the requirement for multiple languages without knowing the language of each call in advance, you should configure the service to use an Automatic Multilingual Configuration (a multilingual model). This allows the Speech service to automatically detect and transcribe multiple spoken languages without requiring you to split files or submit language-specific requests.
Reference:
https://learn.microsoft.com/en-us/azure/ai-services/content-understanding/audio/overview

You can use ChatGPT to summarize text. This can help you understand complex information more easily, summarize the central argument of your own paper, or clarify your research question.
Reference:
https://community.openai.com/t/information-summary-by-using-api/578792