Natural language processing (NLP) lets computers understand, interpret, and respond to human language. It is useful when an application needs to work with what customers write: a support chatbot can answer questions, while a bookshop can make sense of reviews instead of reading each one by hand. Here, NLP concerns text; speech processing is a separate task. section 7.0 section 7.1
For a bookshop, NLP can turn customer language into useful features:
- Recommend books based on reviews and stated preferences. section 7.1
- Summarize new arrivals to help customers discover titles. section 7.1
- Analyze feedback for sentiment to better understand customer opinions. section 7.1
Azure OpenAI Service provides large language models for generating text. Match the model to the job: a small, quick model suits short recommendations, while a larger one may produce a more detailed summary at greater time and cost. Models can only view a limited amount of text at once, so long inputs may need summarization, chunking, or retrieval. Test speed, cost, and quality before production use. section 7.1
C:/> PROMPT="Customer Review: \"I absolutely loved '1984' by George Orwell.
The dystopian setting and the commentary on society were fascinating.
I'm looking for more books like this.\"
Based on this review, recommend three books that the customer might
enjoy, along with a brief description of each."
This prompt pairs a customer's review with a specific request: recommend three books and briefly describe each. It shows how a review can become input to a recommendation feature. section 7.1
An Azure CLI example creates an Azure OpenAI account, deploys a model, and sends a prompt to its endpoint. That setup is only the start: a useful application still needs an appropriate model and input strategy. section 7.1
