You are using Azure OpenAI Service to generate marketing copy. You notice that the output sometimes contains factual inaccuracies about your company's products. Which action can you take to improve factual accuracy?
Providing accurate context in the prompt helps the model generate factual responses.
Why this answer
Including relevant product information in the system message provides the model with authoritative context that grounds its responses in factual data. The system message acts as a persistent instruction set that the model uses to shape its outputs, reducing reliance on its internal training data which may be outdated or incomplete. This technique, known as 'grounding,' directly improves factual accuracy by supplying the model with the specific facts it needs to generate correct marketing copy.
Exam trap
The trap here is that candidates often assume lowering temperature or increasing maxTokens will fix factual accuracy, when in reality these parameters control randomness and output length, not the correctness of the underlying information.
How to eliminate wrong answers
Option A is wrong because lowering the temperature to 0 reduces randomness and creativity but does not inject factual data; it only makes the model more deterministic in its token selection, which can still produce inaccuracies if the model lacks the correct information. Option C is wrong because increasing maxTokens to 4000 only extends the maximum length of the output, which does not address the root cause of factual errors and may even allow the model to generate more incorrect content. Option D is wrong because adding a stop sequence limits where the model stops generating text, which controls output length but does not improve the factual accuracy of the content produced.