A recommendation engine suggests books that fit a customer’s tastes. For a bookstore, an Azure OpenAI prototype can read a customer review and preferences, then suggest similar titles with short descriptions. Natural language processing helps turn what the customer wrote into a useful request. section 7.1
A useful suggestion takes more than a clever prompt:
- Choose a model for the job. A smaller, faster model may suit a quick list of three titles; test speed, cost, and quality before production. section 7.1
- Keep the input manageable. Summarize, chunk, or retrieve relevant text when a review, catalog, or conversation exceeds what the model can view at once. section 7.1
- Check who gets recommended what. Use varied review and reading-history data, measure differences between reader groups, and monitor genre recommendations for drift. section 7.5.1
From a review to three suggestions
A customer says they loved the dystopian setting and social commentary in *1984* and wants more books like it. A prompt asks for three recommendations, each with a brief description. That is a prototype, not yet proof that recommendations will remain fair or useful across all readers. section 7.1 section 7.5.1
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."
The sample prompt shows how a customer’s own words can specify both the taste signal and the requested output. section 7.1
An Azure AI Foundry project can group the datasets, training runs, and deployments for this use case. As the prototype grows, monitoring can track latency, errors, drift, and fairness—not just whether the recommendations sound convincing. section 7.6 section 7.5.1
