AI-102 Implement generative AI solutions Practice Question
You are building a customer support chatbot using Azure OpenAI Service. The chatbot must only respond based on the company's product documentation and should not generate answers outside that scope. Which approach should you use?
⚠ Common exam trap
Test-takers frequently confuse fine-tuning (Option B) with retrieval-augmented generation, assuming that training on documentation will fully constrain the model's output, when in fact fine-tuning does not prevent the model from generating information outside the training data due to its generative nature and lack of explicit retrieval grounding.
Answer choices
Why each option matters
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Use Azure OpenAI On Your Data with a search index built from the documentation.
Azure OpenAI On Your Data allows you to ground the model's responses on a specific set of documents by connecting it to a search index (e.g., Azure Cognitive Search) built from the product documentation. This ensures the model retrieves relevant chunks from the index and generates answers solely based on that content, preventing out-of-scope responses without modifying the underlying model.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Implement content filters to block responses not found in the documentation.
Why it's wrong here
Content filters are designed to block offensive or harmful content, not to enforce domain-specific grounding.
- ✗
Fine-tune a GPT-4 model on the product documentation.
Why it's wrong here
Fine-tuning adapts the model but does not guarantee responses are limited to the documentation; the model can still hallucinate.
- ✓
Use Azure OpenAI On Your Data with a search index built from the documentation.
Why this is correct
This approach grounds the model on the indexed documents, ensuring responses are based on the documentation.
- ✗
Use prompt engineering with a system message instructing the model to only answer from the documentation.
Why it's wrong here
Prompt engineering is insufficient to prevent hallucination; the model may still generate information outside the provided documents.
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